From ce7f7cd1a0605838f361275b5f7b5b1ba9b54cf0 Mon Sep 17 00:00:00 2001
From: Greatharmony <133874863+Greatharmony@users.noreply.github.com>
Date: Wed, 24 Dec 2025 15:10:10 +0900
Subject: [PATCH 1/2] =?UTF-8?q?Week16=5F=EC=98=88=EC=8A=B5=EA=B3=BC?=
 =?UTF-8?q?=EC=A0=9C=5F=EC=9D=B4=EC=8B=9C=ED=98=84?=
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Content-Type: text/plain; charset=UTF-8
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---
 ...354\235\264\354\213\234\355\230\204.ipynb" | 7325 +++++++++++++++++
 ..._\354\235\264\354\213\234\355\230\204.pdf" |  Bin 0 -> 3445026 bytes
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 create mode 100644 "Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\354\235\264\354\213\234\355\230\204.ipynb"
 create mode 100644 "Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\354\235\264\354\213\234\355\230\204.pdf"

diff --git "a/Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\354\235\264\354\213\234\355\230\204.ipynb" "b/Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\354\235\264\354\213\234\355\230\204.ipynb"
new file mode 100644
index 0000000..4964ce6
--- /dev/null
+++ "b/Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\354\235\264\354\213\234\355\230\204.ipynb"
@@ -0,0 +1,7325 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**확률적 경사하강법을 이용한 행렬 분해**"
+      ],
+      "metadata": {
+        "id": "ayjUvpzHHCOE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "id": "czID4-9gG5W9"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "#원본 행렬 R 생성\n",
+        "R = np.array([[4, np.nan, np.nan, 2, np.nan],\n",
+        "              [np.nan, 5, np.nan, 3,1],\n",
+        "              [np.nan, np.nan, 3,4,4],\n",
+        "              [5,2,1,2,np.nan]])\n",
+        "num_users, num_items = R.shape\n",
+        "K = 3\n",
+        "\n",
+        "np.random.seed(1)\n",
+        "P = np.random.normal(scale = 1./K, size = (num_users, K))\n",
+        "Q = np.random.normal(scale = 1./K, size = (num_items, K))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "def get_rmse(R, P, Q, non_zeros):\n",
+        "  error = 0\n",
+        "  full_pred_matrix = np.dot(P, Q.T)\n",
+        "\n",
+        "  x_non_zero_ind = [non_zero[0] for non_zero in non_zeros]\n",
+        "  y_non_zero_ind = [non_zero[1] for non_zero in non_zeros]\n",
+        "  R_non_zeros = R[x_non_zero_ind, y_non_zero_ind]\n",
+        "  full_pred_matrix_non_zeros = full_pred_matrix[x_non_zero_ind, y_non_zero_ind]\n",
+        "  mse = mean_squared_error(R_non_zeros, full_pred_matrix_non_zeros)\n",
+        "  rmse = np.sqrt(mse)"
+      ],
+      "metadata": {
+        "id": "PJ-zo4ALIQlD"
+      },
+      "execution_count": 50,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from os import P_PID\n",
+        "#SGD 기반으로 행렬 분해 수행\n",
+        "non_zeros = [(i,j,R[i,j]) for i in range(num_users) for j in range(num_items) if R[i,j] >0]\n",
+        "\n",
+        "steps = 1000 #SGD를 반복해서 업데이트할 횟수\n",
+        "learning_rate = 0.01 #SGD의 학습률\n",
+        "r_lambda = 0.01 #L2 규제 계수\n",
+        "\n",
+        "for step in range(steps):\n",
+        "  for i, j , r in non_zeros:\n",
+        "    eij = r-np.dot(P[i, :], Q[j,:].T)\n",
+        "    P[i, :] = P[i, :] + learning_rate*(eij * Q[j, :] - r_lambda*P[i, :])\n",
+        "    Q[j, :] = Q[j, :] + learning_rate*(eij * P[i, :] - r_lambda*Q[j,:])\n",
+        "\n",
+        "    rmse = get_rmse(R,P,Q,non_zeros)\n",
+        "    if (step% 50) == 0:\n",
+        "      print(\"### iteration step: \", step, \"rmse: \", rmse)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VNgiqI63KZCj",
+        "outputId": "2040d273-b9d7-46f5-c475-0d0a6eeb39c4"
+      },
+      "execution_count": 51,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "### iteration step:  0 rmse:  None\n",
+            "### iteration step:  0 rmse:  None\n",
+            "### iteration step:  0 rmse:  None\n",
+            "### iteration step:  0 rmse:  None\n",
+            "### iteration step:  0 rmse:  None\n",
+            "### iteration step:  0 rmse:  None\n",
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+            "### iteration step:  0 rmse:  None\n",
+            "### iteration step:  0 rmse:  None\n",
+            "### iteration step:  0 rmse:  None\n",
+            "### iteration step:  50 rmse:  None\n",
+            "### iteration step:  50 rmse:  None\n",
+            "### iteration step:  50 rmse:  None\n",
+            "### iteration step:  50 rmse:  None\n",
+            "### iteration step:  50 rmse:  None\n",
+            "### iteration step:  50 rmse:  None\n",
+            "### iteration step:  50 rmse:  None\n",
+            "### iteration step:  50 rmse:  None\n",
+            "### iteration step:  50 rmse:  None\n",
+            "### iteration step:  50 rmse:  None\n",
+            "### iteration step:  50 rmse:  None\n",
+            "### iteration step:  50 rmse:  None\n",
+            "### iteration step:  100 rmse:  None\n",
+            "### iteration step:  100 rmse:  None\n",
+            "### iteration step:  100 rmse:  None\n",
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+            "### iteration step:  900 rmse:  None\n",
+            "### iteration step:  900 rmse:  None\n",
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+            "### iteration step:  950 rmse:  None\n",
+            "### iteration step:  950 rmse:  None\n",
+            "### iteration step:  950 rmse:  None\n",
+            "### iteration step:  950 rmse:  None\n",
+            "### iteration step:  950 rmse:  None\n",
+            "### iteration step:  950 rmse:  None\n",
+            "### iteration step:  950 rmse:  None\n",
+            "### iteration step:  950 rmse:  None\n",
+            "### iteration step:  950 rmse:  None\n",
+            "### iteration step:  950 rmse:  None\n",
+            "### iteration step:  950 rmse:  None\n",
+            "### iteration step:  950 rmse:  None\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pred_matrix = np.dot(P, Q.T)\n",
+        "print('예측 행렬:\\n', np.round(pred_matrix, 3))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iW6WHXvTMM5X",
+        "outputId": "b2bdb79f-fe91-4fac-abca-47dfadb96ab2"
+      },
+      "execution_count": 52,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "예측 행렬:\n",
+            " [[3.991 0.897 1.306 2.002 1.663]\n",
+            " [6.696 4.978 0.979 2.981 1.003]\n",
+            " [6.677 0.391 2.987 3.977 3.986]\n",
+            " [4.968 2.005 1.006 2.017 1.14 ]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **05. 콘텐츠 기반 필터링 실습-TMDB 5000 영화 데이터 세트**\n",
+        "\n",
+        "**장르 속성을 이용한 영화 콘텐츠 기반 필터링**\n",
+        "* 특정 영화와 비슷한 특성/속성, 구성 요소 등을 가진 다른 영화 추천\n",
+        "* 영화 간의 유사성을 판단하는 기준이 영화를 구성하는 다양한 콘텐츠를 기반으로 하는 방식\n",
+        "\n",
+        "**데이터 로딩 및 가공**"
+      ],
+      "metadata": {
+        "id": "h7poBF8WG7ng"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import warnings; warnings.filterwarnings('ignore')\n",
+        "\n",
+        "movies = pd.read_csv('/content/tmdb_5000_movies.csv')\n",
+        "print(movies.shape)\n",
+        "movies.head(1)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 327
+        },
+        "id": "KILZrMfNH3oN",
+        "outputId": "eb8081ba-3f9d-48e7-81e0-d36c01b1be20"
+      },
+      "execution_count": 53,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(4803, 20)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      budget  \\\n",
+              "0  237000000   \n",
+              "\n",
+              "                                                                                                genres  \\\n",
+              "0  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {...   \n",
+              "\n",
+              "                      homepage     id  \\\n",
+              "0  http://www.avatarmovie.com/  19995   \n",
+              "\n",
+              "                                                                                              keywords  \\\n",
+              "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\": 2964, \"name\": \"future\"}, {\"id\": 3386, \"name\": \"sp...   \n",
+              "\n",
+              "  original_language original_title  \\\n",
+              "0                en         Avatar   \n",
+              "\n",
+              "                                                                                              overview  \\\n",
+              "0  In the 22nd century, a paraplegic Marine is dispatched to the moon Pandora on a unique mission, ...   \n",
+              "\n",
+              "   popularity  \\\n",
+              "0  150.437577   \n",
+              "\n",
+              "                                                                                  production_companies  \\\n",
+              "0  [{\"name\": \"Ingenious Film Partners\", \"id\": 289}, {\"name\": \"Twentieth Century Fox Film Corporatio...   \n",
+              "\n",
+              "                                                                                  production_countries  \\\n",
+              "0  [{\"iso_3166_1\": \"US\", \"name\": \"United States of America\"}, {\"iso_3166_1\": \"GB\", \"name\": \"United ...   \n",
+              "\n",
+              "  release_date     revenue  runtime  \\\n",
+              "0   2009-12-10  2787965087    162.0   \n",
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+              "0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso_639_1\": \"es\", \"name\": \"Espa\\u00f1ol\"}]   \n",
+              "\n",
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+              "0  Released  Enter the World of Pandora.  Avatar           7.2       11800  "
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+              "      <td>http://www.avatarmovie.com/</td>\n",
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+              "      <td>150.437577</td>\n",
+              "      <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289}, {\"name\": \"Twentieth Century Fox Film Corporatio...</td>\n",
+              "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States of America\"}, {\"iso_3166_1\": \"GB\", \"name\": \"United ...</td>\n",
+              "      <td>2009-12-10</td>\n",
+              "      <td>2787965087</td>\n",
+              "      <td>162.0</td>\n",
+              "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso_639_1\": \"es\", \"name\": \"Espa\\u00f1ol\"}]</td>\n",
+              "      <td>Released</td>\n",
+              "      <td>Enter the World of Pandora.</td>\n",
+              "      <td>Avatar</td>\n",
+              "      <td>7.2</td>\n",
+              "      <td>11800</td>\n",
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+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
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+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
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+              "      width: 32px;\n",
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+              "\n",
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+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
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+              "\n",
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+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
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+              "      fill: #D2E3FC;\n",
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+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "\n",
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+              "        document.querySelector('#df-f3f67cef-9c83-41ff-a59e-dc28c6859c91 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
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+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "movies",
+              "summary": "{\n  \"name\": \"movies\",\n  \"rows\": 4803,\n  \"fields\": [\n    {\n      \"column\": \"budget\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40722391,\n        \"min\": 0,\n        \"max\": 380000000,\n        \"num_unique_values\": 436,\n        \"samples\": [\n          439000,\n          68000000,\n          700000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1175,\n        \"samples\": [\n          \"[{\\\"id\\\": 14, \\\"name\\\": \\\"Fantasy\\\"}, {\\\"id\\\": 12, \\\"name\\\": \\\"Adventure\\\"}, {\\\"id\\\": 16, \\\"name\\\": \\\"Animation\\\"}]\",\n          \"[{\\\"id\\\": 28, \\\"name\\\": \\\"Action\\\"}, {\\\"id\\\": 35, \\\"name\\\": \\\"Comedy\\\"}, {\\\"id\\\": 80, \\\"name\\\": \\\"Crime\\\"}, {\\\"id\\\": 18, \\\"name\\\": \\\"Drama\\\"}]\",\n          \"[{\\\"id\\\": 12, \\\"name\\\": \\\"Adventure\\\"}, {\\\"id\\\": 16, \\\"name\\\": \\\"Animation\\\"}, {\\\"id\\\": 10751, \\\"name\\\": \\\"Family\\\"}, {\\\"id\\\": 14, \\\"name\\\": \\\"Fantasy\\\"}, {\\\"id\\\": 878, \\\"name\\\": \\\"Science Fiction\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"homepage\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1691,\n        \"samples\": [\n          \"https://www.warnerbros.com/running-scared\",\n          \"http://www.51birchstreet.com/index.php\",\n          \"http://movies2.foxjapan.com/glee/\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88694,\n        \"min\": 5,\n        \"max\": 459488,\n        \"num_unique_values\": 4803,\n        \"samples\": [\n          8427,\n          13006,\n          18041\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keywords\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4222,\n        \"samples\": [\n          \"[{\\\"id\\\": 782, \\\"name\\\": \\\"assassin\\\"}, {\\\"id\\\": 1872, \\\"name\\\": \\\"loss of father\\\"}, {\\\"id\\\": 2908, \\\"name\\\": \\\"secret society\\\"}, {\\\"id\\\": 3045, \\\"name\\\": \\\"mission of murder\\\"}, {\\\"id\\\": 9748, \\\"name\\\": \\\"revenge\\\"}]\",\n          \"[{\\\"id\\\": 2987, \\\"name\\\": \\\"gang war\\\"}, {\\\"id\\\": 4942, \\\"name\\\": \\\"victim of murder\\\"}, {\\\"id\\\": 5332, \\\"name\\\": \\\"greed\\\"}, {\\\"id\\\": 6062, \\\"name\\\": \\\"hostility\\\"}, {\\\"id\\\": 156212, \\\"name\\\": \\\"spaghetti western\\\"}]\",\n          \"[{\\\"id\\\": 703, \\\"name\\\": \\\"detective\\\"}, {\\\"id\\\": 1299, \\\"name\\\": \\\"monster\\\"}, {\\\"id\\\": 6101, \\\"name\\\": \\\"engine\\\"}, {\\\"id\\\": 10988, \\\"name\\\": \\\"based on tv series\\\"}, {\\\"id\\\": 15162, \\\"name\\\": \\\"dog\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_language\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 37,\n        \"samples\": [\n          \"xx\",\n          \"ta\",\n          \"es\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4801,\n        \"samples\": [\n          \"I Spy\",\n          \"Love Letters\",\n          \"Sleepover\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"overview\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4800,\n        \"samples\": [\n          \"When the Switchblade, the most sophisticated prototype stealth fighter created yet, is stolen from the U.S. government, one of the United States' top spies, Alex Scott, is called to action. What he doesn't expect is to get teamed up with a cocky civilian, World Class Boxing Champion Kelly Robinson, on a dangerous top secret espionage mission. Their assignment: using equal parts skill and humor, catch Arnold Gundars, one of the world's most successful arms dealers.\",\n          \"When \\\"street smart\\\" rapper Christopher \\\"C-Note\\\" Hawkins (Big Boi) applies for a membership to all-white Carolina Pines Country Club, the establishment's proprietors are hardly ready to oblige him.\",\n          \"As their first year of high school looms ahead, best friends Julie, Hannah, Yancy and Farrah have one last summer sleepover. Little do they know they're about to embark on the adventure of a lifetime. Desperate to shed their nerdy status, they take part in a night-long scavenger hunt that pits them against their popular archrivals. Everything under the sun goes on -- from taking Yancy's father's car to sneaking into nightclubs!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"popularity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31.816649749537806,\n        \"min\": 0.0,\n        \"max\": 875.581305,\n        \"num_unique_values\": 4802,\n        \"samples\": [\n          13.267631,\n          0.010909,\n          5.842299\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"production_companies\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3697,\n        \"samples\": [\n          \"[{\\\"name\\\": \\\"Paramount Pictures\\\", \\\"id\\\": 4}, {\\\"name\\\": \\\"Cherry Alley Productions\\\", \\\"id\\\": 2232}]\",\n          \"[{\\\"name\\\": \\\"Twentieth Century Fox Film Corporation\\\", \\\"id\\\": 306}, {\\\"name\\\": \\\"Dune Entertainment\\\", \\\"id\\\": 444}, {\\\"name\\\": \\\"Regency Enterprises\\\", \\\"id\\\": 508}, {\\\"name\\\": \\\"Guy Walks into a Bar Productions\\\", \\\"id\\\": 2645}, {\\\"name\\\": \\\"Deep River Productions\\\", \\\"id\\\": 2646}, {\\\"name\\\": \\\"Friendly Films (II)\\\", \\\"id\\\": 81136}]\",\n          \"[{\\\"name\\\": \\\"Twentieth Century Fox Film Corporation\\\", \\\"id\\\": 306}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"production_countries\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 469,\n        \"samples\": [\n          \"[{\\\"iso_3166_1\\\": \\\"ES\\\", \\\"name\\\": \\\"Spain\\\"}, {\\\"iso_3166_1\\\": \\\"GB\\\", \\\"name\\\": \\\"United Kingdom\\\"}, {\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}, {\\\"iso_3166_1\\\": \\\"FR\\\", \\\"name\\\": \\\"France\\\"}]\",\n          \"[{\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}, {\\\"iso_3166_1\\\": \\\"CA\\\", \\\"name\\\": \\\"Canada\\\"}, {\\\"iso_3166_1\\\": \\\"DE\\\", \\\"name\\\": \\\"Germany\\\"}]\",\n          \"[{\\\"iso_3166_1\\\": \\\"DE\\\", \\\"name\\\": \\\"Germany\\\"}, {\\\"iso_3166_1\\\": \\\"ES\\\", \\\"name\\\": \\\"Spain\\\"}, {\\\"iso_3166_1\\\": \\\"GB\\\", \\\"name\\\": \\\"United Kingdom\\\"}, {\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"release_date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 3280,\n        \"samples\": [\n          \"1966-10-16\",\n          \"1987-07-31\",\n          \"1993-09-23\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"revenue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 162857100,\n        \"min\": 0,\n        \"max\": 2787965087,\n        \"num_unique_values\": 3297,\n        \"samples\": [\n          11833696,\n          10462500,\n          17807569\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"runtime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.611934588844207,\n        \"min\": 0.0,\n        \"max\": 338.0,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          74.0,\n          85.0,\n          170.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spoken_languages\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 544,\n        \"samples\": [\n          \"[{\\\"iso_639_1\\\": \\\"es\\\", \\\"name\\\": \\\"Espa\\\\u00f1ol\\\"}, {\\\"iso_639_1\\\": \\\"en\\\", \\\"name\\\": \\\"English\\\"}, {\\\"iso_639_1\\\": \\\"fr\\\", \\\"name\\\": \\\"Fran\\\\u00e7ais\\\"}, {\\\"iso_639_1\\\": \\\"hu\\\", \\\"name\\\": \\\"Magyar\\\"}]\",\n          \"[{\\\"iso_639_1\\\": \\\"en\\\", \\\"name\\\": \\\"English\\\"}, {\\\"iso_639_1\\\": \\\"it\\\", \\\"name\\\": \\\"Italiano\\\"}, {\\\"iso_639_1\\\": \\\"pt\\\", \\\"name\\\": \\\"Portugu\\\\u00eas\\\"}]\",\n          \"[{\\\"iso_639_1\\\": \\\"de\\\", \\\"name\\\": \\\"Deutsch\\\"}, {\\\"iso_639_1\\\": \\\"it\\\", \\\"name\\\": \\\"Italiano\\\"}, {\\\"iso_639_1\\\": \\\"la\\\", \\\"name\\\": \\\"Latin\\\"}, {\\\"iso_639_1\\\": \\\"pl\\\", \\\"name\\\": \\\"Polski\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Released\",\n          \"Post Production\",\n          \"Rumored\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tagline\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3944,\n        \"samples\": [\n          \"When you're 17, every day is war.\",\n          \"An Unspeakable Horror. A Creative Genius. Captured For Eternity.\",\n          \"May the schwartz be with you\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4800,\n        \"samples\": [\n          \"I Spy\",\n          \"Who's Your Caddy?\",\n          \"Sleepover\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1946121628478925,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          5.1,\n          7.2,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1234,\n        \"min\": 0,\n        \"max\": 13752,\n        \"num_unique_values\": 1609,\n        \"samples\": [\n          7604,\n          3428,\n          225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 53
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "movies_df = movies[['id', 'title', 'genres', 'vote_average', 'vote_count', 'popularity', 'keywords', 'overview']]"
+      ],
+      "metadata": {
+        "id": "5ElyMDBcIMde"
+      },
+      "execution_count": 54,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* ‘genres’, ‘keywords’ 등과 같은 칼럼을 보면 [{\"id\"： 28, \"name\"： \"Action\"}, {\"id\"： 12, \"name\"：\n",
+        "\"Adventure\"}]와 같이 파이썬 리스트(list) 내부에 여러 개의 딕셔너리(diet)가 있는 형태의 문자열로\n",
+        "표기돼 있음"
+      ],
+      "metadata": {
+        "id": "b11_PCmcMqko"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pd.set_option('max_colwidth', 100)\n",
+        "movies_df[['genres', 'keywords']][:1]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81
+        },
+        "id": "hiMmTFFkNDCX",
+        "outputId": "da89b87b-2ab3-4811-a8ea-dbf20cbf251d"
+      },
+      "execution_count": 55,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                                                                genres  \\\n",
+              "0  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {...   \n",
+              "\n",
+              "                                                                                              keywords  \n",
+              "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\": 2964, \"name\": \"future\"}, {\"id\": 3386, \"name\": \"sp...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3486f4bc-13e1-4e96-84c5-9dab6832598b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>genres</th>\n",
+              "      <th>keywords</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {...</td>\n",
+              "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\": 2964, \"name\": \"future\"}, {\"id\": 3386, \"name\": \"sp...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3486f4bc-13e1-4e96-84c5-9dab6832598b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-3486f4bc-13e1-4e96-84c5-9dab6832598b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-3486f4bc-13e1-4e96-84c5-9dab6832598b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"movies_df[['genres', 'keywords']][:1]\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"[{\\\"id\\\": 28, \\\"name\\\": \\\"Action\\\"}, {\\\"id\\\": 12, \\\"name\\\": \\\"Adventure\\\"}, {\\\"id\\\": 14, \\\"name\\\": \\\"Fantasy\\\"}, {\\\"id\\\": 878, \\\"name\\\": \\\"Science Fiction\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keywords\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"[{\\\"id\\\": 1463, \\\"name\\\": \\\"culture clash\\\"}, {\\\"id\\\": 2964, \\\"name\\\": \\\"future\\\"}, {\\\"id\\\": 3386, \\\"name\\\": \\\"space war\\\"}, {\\\"id\\\": 3388, \\\"name\\\": \\\"space colony\\\"}, {\\\"id\\\": 3679, \\\"name\\\": \\\"society\\\"}, {\\\"id\\\": 3801, \\\"name\\\": \\\"space travel\\\"}, {\\\"id\\\": 9685, \\\"name\\\": \\\"futuristic\\\"}, {\\\"id\\\": 9840, \\\"name\\\": \\\"romance\\\"}, {\\\"id\\\": 9882, \\\"name\\\": \\\"space\\\"}, {\\\"id\\\": 9951, \\\"name\\\": \\\"alien\\\"}, {\\\"id\\\": 10148, \\\"name\\\": \\\"tribe\\\"}, {\\\"id\\\": 10158, \\\"name\\\": \\\"alien planet\\\"}, {\\\"id\\\": 10987, \\\"name\\\": \\\"cgi\\\"}, {\\\"id\\\": 11399, \\\"name\\\": \\\"marine\\\"}, {\\\"id\\\": 13065, \\\"name\\\": \\\"soldier\\\"}, {\\\"id\\\": 14643, \\\"name\\\": \\\"battle\\\"}, {\\\"id\\\": 14720, \\\"name\\\": \\\"love affair\\\"}, {\\\"id\\\": 165431, \\\"name\\\": \\\"anti war\\\"}, {\\\"id\\\": 193554, \\\"name\\\": \\\"power relations\\\"}, {\\\"id\\\": 206690, \\\"name\\\": \\\"mind and soul\\\"}, {\\\"id\\\": 209714, \\\"name\\\": \\\"3d\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 55
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from ast import literal_eval\n",
+        "import numpy as np\n",
+        "\n",
+        "def safe_literal_eval(val):\n",
+        "    if isinstance(val, str):\n",
+        "        try:\n",
+        "            return literal_eval(val)\n",
+        "        except (ValueError, SyntaxError):\n",
+        "            return []  # Return empty list for malformed strings\n",
+        "    elif val is np.nan:\n",
+        "        return [] # Handle NaN values explicitly, which are float type\n",
+        "    else:\n",
+        "        return [] # Handle any other unexpected types\n",
+        "\n",
+        "movies_df['genres'] = movies_df['genres'].apply(safe_literal_eval)\n",
+        "movies_df['keywords'] = movies_df['keywords'].apply(safe_literal_eval)"
+      ],
+      "metadata": {
+        "id": "BbiyDYBFcnrf"
+      },
+      "execution_count": 56,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "movies_df['genres'] = movies_df['genres'].apply(lambda x: [y['name'] for y in x])\n",
+        "movies_df['keywords'] = movies_df['keywords'].apply(lambda x: [y['name'] for y in x])\n",
+        "movies_df[['genres', 'keywords']][:1]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81
+        },
+        "id": "xTnEmykJebTh",
+        "outputId": "76317e1c-075e-4bd0-a36b-c25f69ba9656"
+      },
+      "execution_count": 57,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                          genres  \\\n",
+              "0  [Action, Adventure, Fantasy, Science Fiction]   \n",
+              "\n",
+              "                                                                                              keywords  \n",
+              "0  [culture clash, future, space war, space colony, society, space travel, futuristic, romance, spa...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-96c94230-49a2-414b-954f-9a8b388805d6\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>genres</th>\n",
+              "      <th>keywords</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
+              "      <td>[culture clash, future, space war, space colony, society, space travel, futuristic, romance, spa...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96c94230-49a2-414b-954f-9a8b388805d6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-96c94230-49a2-414b-954f-9a8b388805d6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-96c94230-49a2-414b-954f-9a8b388805d6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"movies_df[['genres', 'keywords']][:1]\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keywords\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 57
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**장르 콘텐츠 유사도 측정**\n",
+        "* 문자열로 변환된 genres 칼럼을 Count 기반으로 피처 벡터화 변환합니다.\n",
+        "* genres 문자열을 피처 벡터화 행렬로 변환한 데이터 세트로 코사인 유사도를 통해 비교합니다. 이를 위해 데이터 세트의 레크드별로 타 레코드와 장르에서 코사인 유사도 값을 가지는 객체를 생성합니다.\n",
+        "* 장르 유사도가 높은 영화 중에 평점이 높은 순으로 영화를 추천합니다,\n"
+      ],
+      "metadata": {
+        "id": "BKDGHUopfZf3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.feature_extraction.text import CountVectorizer\n",
+        "\n",
+        "#CountVectorizer를 적용하기 위해 공백 문자로 word 단위가 구분되는 문자열로 변환\n",
+        "movies_df['genres_literal'] = movies_df['genres'].apply(lambda x:(' ').join(x))\n",
+        "count_vect = CountVectorizer(min_df = 1, ngram_range=(1,2)) # min_df를 0에서 1로 수정\n",
+        "genre_mat = count_vect.fit_transform(movies_df['genres_literal'])\n",
+        "print(genre_mat.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bTjG5FRJgcEZ",
+        "outputId": "be2b0e75-3b67-4908-d448-2e675d4cb1bb"
+      },
+      "execution_count": 58,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(4803, 276)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "![화면 캡처 2025-12-23 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)"
+      ],
+      "metadata": {
+        "id": "oLDUmAkAi2dt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics.pairwise import cosine_similarity\n",
+        "\n",
+        "genre_sim = cosine_similarity(genre_mat, genre_mat)\n",
+        "print(genre_sim.shape)\n",
+        "print(genre_sim[:2])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gd4dp9hRi4rR",
+        "outputId": "d8f53414-31e1-43e6-e05b-6b0c098f85f8"
+      },
+      "execution_count": 59,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(4803, 4803)\n",
+            "[[1.         0.59628479 0.4472136  ... 0.         0.         0.        ]\n",
+            " [0.59628479 1.         0.4        ... 0.         0.         0.        ]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "genre_sim_sorted_ind = genre_sim.argsort()[:, ::-1]\n",
+        "print(genre_sim_sorted_ind[:1]) #유사도가 높은 순으로 정리된 genre_sim 객체의 비교 행 위치 인덱스 값을 간편하게 얻음"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "v7bPtl5ljscw",
+        "outputId": "0eced92a-d67d-4d3a-fe39-f592e2e393b7"
+      },
+      "execution_count": 60,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[   0 3494  813 ... 3038 3037 2401]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**장르 콘텐츠 필터링을 이용한 영화추천**"
+      ],
+      "metadata": {
+        "id": "cX1SHNLcnUSM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def find_sim_movie(df, sorted_ind, title_name, top_n = 10):\n",
+        "  title_movie = df[df['title'] == title_name]\n",
+        "\n",
+        "  title_index = title_movie.index.values\n",
+        "  similar_indexes = sorted_ind[title_index, :(top_n)]\n",
+        "\n",
+        "  print(similar_indexes)\n",
+        "  similar_indexes = similar_indexes.reshape(-1)\n",
+        "\n",
+        "  return df.iloc[similar_indexes]"
+      ],
+      "metadata": {
+        "id": "EwMpgfulnXQR"
+      },
+      "execution_count": 61,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "similar_movies = find_sim_movie(movies_df, genre_sim_sorted_ind, 'The Godfather', 10)\n",
+        "similar_movies[['title', 'vote_average']]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 381
+        },
+        "id": "gFbJliblnw-a",
+        "outputId": "731a4796-60ca-4025-fca8-f558a272af76"
+      },
+      "execution_count": 62,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[2731 1243 3636 1946 2640 4065 1847 4217  883 3866]]\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                               title  vote_average\n",
+              "2731                          The Godfather: Part II           8.3\n",
+              "1243                                    Mean Streets           7.2\n",
+              "3636                                   Light Sleeper           5.7\n",
+              "1946  The Bad Lieutenant: Port of Call - New Orleans           6.0\n",
+              "2640         Things to Do in Denver When You're Dead           6.7\n",
+              "4065                                      Mi America           0.0\n",
+              "1847                                      GoodFellas           8.2\n",
+              "4217                                            Kids           6.8\n",
+              "883                              Catch Me If You Can           7.7\n",
+              "3866                                     City of God           8.1"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-207d3db4-778e-4ed0-afd2-26697a7f21d2\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>title</th>\n",
+              "      <th>vote_average</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2731</th>\n",
+              "      <td>The Godfather: Part II</td>\n",
+              "      <td>8.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1243</th>\n",
+              "      <td>Mean Streets</td>\n",
+              "      <td>7.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3636</th>\n",
+              "      <td>Light Sleeper</td>\n",
+              "      <td>5.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1946</th>\n",
+              "      <td>The Bad Lieutenant: Port of Call - New Orleans</td>\n",
+              "      <td>6.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2640</th>\n",
+              "      <td>Things to Do in Denver When You're Dead</td>\n",
+              "      <td>6.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4065</th>\n",
+              "      <td>Mi America</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1847</th>\n",
+              "      <td>GoodFellas</td>\n",
+              "      <td>8.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4217</th>\n",
+              "      <td>Kids</td>\n",
+              "      <td>6.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>883</th>\n",
+              "      <td>Catch Me If You Can</td>\n",
+              "      <td>7.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3866</th>\n",
+              "      <td>City of God</td>\n",
+              "      <td>8.1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-207d3db4-778e-4ed0-afd2-26697a7f21d2')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-207d3db4-778e-4ed0-afd2-26697a7f21d2 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-207d3db4-778e-4ed0-afd2-26697a7f21d2');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-241fbe60-fe4b-45e0-9dfd-74c6bd3f8f94\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-241fbe60-fe4b-45e0-9dfd-74c6bd3f8f94')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-241fbe60-fe4b-45e0-9dfd-74c6bd3f8f94 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"similar_movies[['title', 'vote_average']]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Catch Me If You Can\",\n          \"Mean Streets\",\n          \"Mi America\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4467892793981623,\n        \"min\": 0.0,\n        \"max\": 8.3,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          7.7,\n          7.2,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 62
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "movies_df[['title', 'vote_average', 'vote_count']].sort_values('vote_average', ascending=False)[:10]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "id": "NoHMGc3Iqdpu",
+        "outputId": "066d1418-5b08-4844-81f2-04065170a341"
+      },
+      "execution_count": 63,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                         title  vote_average  vote_count\n",
+              "3519          Stiff Upper Lips          10.0           1\n",
+              "4247     Me You and Five Bucks          10.0           2\n",
+              "4045     Dancer, Texas Pop. 81          10.0           1\n",
+              "4662            Little Big Top          10.0           1\n",
+              "3992                 Sardaarji           9.5           2\n",
+              "2386            One Man's Hero           9.3           2\n",
+              "2970        There Goes My Baby           8.5           2\n",
+              "1881  The Shawshank Redemption           8.5        8205\n",
+              "2796     The Prisoner of Zenda           8.4          11\n",
+              "3337             The Godfather           8.4        5893"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a8f03ffd-ac63-48e7-bd25-f4935adfab32\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>title</th>\n",
+              "      <th>vote_average</th>\n",
+              "      <th>vote_count</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>3519</th>\n",
+              "      <td>Stiff Upper Lips</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4247</th>\n",
+              "      <td>Me You and Five Bucks</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4045</th>\n",
+              "      <td>Dancer, Texas Pop. 81</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4662</th>\n",
+              "      <td>Little Big Top</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3992</th>\n",
+              "      <td>Sardaarji</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2386</th>\n",
+              "      <td>One Man's Hero</td>\n",
+              "      <td>9.3</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2970</th>\n",
+              "      <td>There Goes My Baby</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1881</th>\n",
+              "      <td>The Shawshank Redemption</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>8205</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2796</th>\n",
+              "      <td>The Prisoner of Zenda</td>\n",
+              "      <td>8.4</td>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3337</th>\n",
+              "      <td>The Godfather</td>\n",
+              "      <td>8.4</td>\n",
+              "      <td>5893</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a8f03ffd-ac63-48e7-bd25-f4935adfab32')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a8f03ffd-ac63-48e7-bd25-f4935adfab32 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a8f03ffd-ac63-48e7-bd25-f4935adfab32');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-9d2056db-c049-4806-a9c6-ff876a5e509f\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9d2056db-c049-4806-a9c6-ff876a5e509f')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-9d2056db-c049-4806-a9c6-ff876a5e509f button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"movies_df[['title', 'vote_average', 'vote_count']]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"The Prisoner of Zenda\",\n          \"Me You and Five Bucks\",\n          \"One Man's Hero\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7366591251499343,\n        \"min\": 8.4,\n        \"max\": 10.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9.5,\n          8.4,\n          9.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3020,\n        \"min\": 1,\n        \"max\": 8205,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5893,\n          8205\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 63
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* 왜곡된 평점 데이터를 회피할 수 있도록 평점에 평가 횟수를 반영할 수 있는 새로운 평가 방식이 필요\n",
+        "\n",
+        "\n",
+        "```\n",
+        "가중평점(Weighted Rating) = (v/(v+m))*R +(m/(v+m))*C\n",
+        "```\n",
+        "* v: 개별 영화에 평점을 투표한 횟수: vote_count\n",
+        "* m: 평점을 부여하기 위한 최소 투표 횟수\n",
+        "* R: 개별 영화에 대한 평균 평점: vote_average\n",
+        "* C: 젼체 영화에 대한 평균 평점\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "MNXToqXeq3Hk"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "C = movies_df['vote_average'].mean()\n",
+        "m = movies_df['vote_count'].quantile(0.6)\n",
+        "print('C:',  round(C,3), 'm:', round(m,3))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Gp4RH5-IrVqD",
+        "outputId": "54ed8ae4-fe1c-4e14-a2aa-705c5e60f94a"
+      },
+      "execution_count": 64,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "C: 6.092 m: 370.2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "percentile = 0.6\n",
+        "m = movies_df['vote_count'].quantile(percentile)\n",
+        "C = movies_df['vote_average'].mean()\n",
+        "\n",
+        "def weighted_vote_average(record):\n",
+        "  v = record['vote_count']\n",
+        "  R = record['vote_average']\n",
+        "\n",
+        "  return ((v/(v+m))*R) + ((m/(m+v))*C)\n",
+        "\n",
+        "movies_df['weighted_vote'] = movies.apply(weighted_vote_average, axis =1)"
+      ],
+      "metadata": {
+        "id": "CL3Q42bPrsJP"
+      },
+      "execution_count": 65,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "movies_df[['title', 'vote_average', 'weighted_vote', 'vote_count']].sort_values('weighted_vote', ascending = False)[:10]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "id": "Ac2BxbuXsLU1",
+        "outputId": "c1a2355c-82cd-4249-dcc9-bfb2cfe04baf"
+      },
+      "execution_count": 66,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                         title  vote_average  weighted_vote  vote_count\n",
+              "1881  The Shawshank Redemption           8.5       8.396052        8205\n",
+              "3337             The Godfather           8.4       8.263591        5893\n",
+              "662                 Fight Club           8.3       8.216455        9413\n",
+              "3232              Pulp Fiction           8.3       8.207102        8428\n",
+              "65             The Dark Knight           8.2       8.136930       12002\n",
+              "1818          Schindler's List           8.3       8.126069        4329\n",
+              "3865                  Whiplash           8.3       8.123248        4254\n",
+              "809               Forrest Gump           8.2       8.105954        7927\n",
+              "2294             Spirited Away           8.3       8.105867        3840\n",
+              "2731    The Godfather: Part II           8.3       8.079586        3338"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8a0ef3f4-c14d-46f1-a311-a8a924606a97\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>title</th>\n",
+              "      <th>vote_average</th>\n",
+              "      <th>weighted_vote</th>\n",
+              "      <th>vote_count</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1881</th>\n",
+              "      <td>The Shawshank Redemption</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>8.396052</td>\n",
+              "      <td>8205</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3337</th>\n",
+              "      <td>The Godfather</td>\n",
+              "      <td>8.4</td>\n",
+              "      <td>8.263591</td>\n",
+              "      <td>5893</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>662</th>\n",
+              "      <td>Fight Club</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.216455</td>\n",
+              "      <td>9413</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3232</th>\n",
+              "      <td>Pulp Fiction</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.207102</td>\n",
+              "      <td>8428</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>65</th>\n",
+              "      <td>The Dark Knight</td>\n",
+              "      <td>8.2</td>\n",
+              "      <td>8.136930</td>\n",
+              "      <td>12002</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1818</th>\n",
+              "      <td>Schindler's List</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.126069</td>\n",
+              "      <td>4329</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3865</th>\n",
+              "      <td>Whiplash</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.123248</td>\n",
+              "      <td>4254</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>809</th>\n",
+              "      <td>Forrest Gump</td>\n",
+              "      <td>8.2</td>\n",
+              "      <td>8.105954</td>\n",
+              "      <td>7927</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2294</th>\n",
+              "      <td>Spirited Away</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.105867</td>\n",
+              "      <td>3840</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2731</th>\n",
+              "      <td>The Godfather: Part II</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.079586</td>\n",
+              "      <td>3338</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a0ef3f4-c14d-46f1-a311-a8a924606a97')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8a0ef3f4-c14d-46f1-a311-a8a924606a97 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8a0ef3f4-c14d-46f1-a311-a8a924606a97');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-0002847b-2f43-4310-ba1f-b9badbb5ad92\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0002847b-2f43-4310-ba1f-b9badbb5ad92')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-0002847b-2f43-4310-ba1f-b9badbb5ad92 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"movies_df[['title', 'vote_average', 'weighted_vote', 'vote_count']]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Spirited Away\",\n          \"The Godfather\",\n          \"Schindler's List\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08755950357709151,\n        \"min\": 8.2,\n        \"max\": 8.5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8.4,\n          8.2,\n          8.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weighted_vote\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09696608479450805,\n        \"min\": 8.07958629828635,\n        \"max\": 8.39605162693645,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          8.105867158639835,\n          8.263590802034972,\n          8.126068673669016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2866,\n        \"min\": 3338,\n        \"max\": 12002,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3840,\n          5893,\n          4329\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 66
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def find_sim_movie(df, sorted_ind, title_name, top_n = 10):\n",
+        "  title_movie = df[df['title'] == title_name]\n",
+        "  title_index = title_movie.index.values\n",
+        "\n",
+        "  similar_indexes = sorted_ind[title_index, :(top_n*2)]\n",
+        "  similar_indexes = similar_indexes.reshape(-1)\n",
+        "  similar_indexes = similar_indexes[similar_indexes != title_index]\n",
+        "\n",
+        "  return df.iloc[similar_indexes].sort_values('weighted_vote', ascending=False)[:top_n]\n",
+        "\n",
+        "similar_movies = find_sim_movie(movies_df, genre_sim_sorted_ind, 'The Godfather', 10)\n",
+        "similar_movies[['title', 'vote_average', 'weighted_vote']]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "id": "9saSifP2s7xV",
+        "outputId": "1fb82f69-2144-4452-e5df-202d58bd8da6"
+      },
+      "execution_count": 67,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                            title  vote_average  weighted_vote\n",
+              "2731       The Godfather: Part II           8.3       8.079586\n",
+              "1847                   GoodFellas           8.2       7.976937\n",
+              "3866                  City of God           8.1       7.759693\n",
+              "1663  Once Upon a Time in America           8.2       7.657811\n",
+              "883           Catch Me If You Can           7.7       7.557097\n",
+              "281             American Gangster           7.4       7.141396\n",
+              "4041              This Is England           7.4       6.739664\n",
+              "1149              American Hustle           6.8       6.717525\n",
+              "1243                 Mean Streets           7.2       6.626569\n",
+              "2839                     Rounders           6.9       6.530427"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d1eea6ce-5700-4bfe-b8e6-b233e5519c04\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>title</th>\n",
+              "      <th>vote_average</th>\n",
+              "      <th>weighted_vote</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2731</th>\n",
+              "      <td>The Godfather: Part II</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.079586</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1847</th>\n",
+              "      <td>GoodFellas</td>\n",
+              "      <td>8.2</td>\n",
+              "      <td>7.976937</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3866</th>\n",
+              "      <td>City of God</td>\n",
+              "      <td>8.1</td>\n",
+              "      <td>7.759693</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1663</th>\n",
+              "      <td>Once Upon a Time in America</td>\n",
+              "      <td>8.2</td>\n",
+              "      <td>7.657811</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>883</th>\n",
+              "      <td>Catch Me If You Can</td>\n",
+              "      <td>7.7</td>\n",
+              "      <td>7.557097</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>281</th>\n",
+              "      <td>American Gangster</td>\n",
+              "      <td>7.4</td>\n",
+              "      <td>7.141396</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4041</th>\n",
+              "      <td>This Is England</td>\n",
+              "      <td>7.4</td>\n",
+              "      <td>6.739664</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1149</th>\n",
+              "      <td>American Hustle</td>\n",
+              "      <td>6.8</td>\n",
+              "      <td>6.717525</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1243</th>\n",
+              "      <td>Mean Streets</td>\n",
+              "      <td>7.2</td>\n",
+              "      <td>6.626569</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2839</th>\n",
+              "      <td>Rounders</td>\n",
+              "      <td>6.9</td>\n",
+              "      <td>6.530427</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1eea6ce-5700-4bfe-b8e6-b233e5519c04')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d1eea6ce-5700-4bfe-b8e6-b233e5519c04 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d1eea6ce-5700-4bfe-b8e6-b233e5519c04');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-0d34661f-eb2a-4c8b-a7fe-b5a71810be57\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0d34661f-eb2a-4c8b-a7fe-b5a71810be57')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-0d34661f-eb2a-4c8b-a7fe-b5a71810be57 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"similar_movies[['title', 'vote_average', 'weighted_vote']]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Mean Streets\",\n          \"GoodFellas\",\n          \"American Gangster\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5613475849338901,\n        \"min\": 6.8,\n        \"max\": 8.3,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.2,\n          6.8,\n          8.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weighted_vote\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5954507780784589,\n        \"min\": 6.530427473190107,\n        \"max\": 8.07958629828635,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          6.626568667932654,\n          7.976937256676415,\n          7.1413961709782265\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 67
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **06.아이템 기반 최근접 이웃 협업 필터링 실습**\n",
+        "**데이터 가공 및 변환**"
+      ],
+      "metadata": {
+        "id": "EPvqs026vyzE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "movies = pd.read_csv('/content/movies.csv')\n",
+        "ratings = pd.read_csv('/content/ratings.csv')\n",
+        "print(movies.shape)\n",
+        "print(ratings.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZRnCRT66v76Y",
+        "outputId": "ae6766d3-608c-49c5-dc23-9bf7f17d6ba1"
+      },
+      "execution_count": 68,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(9742, 3)\n",
+            "(100836, 4)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* 사용자를 로우로, 모든 영화를 칼럼으로 구성한 데이터 세트로 변경\n",
+        "* DataFrame의 pivot_table() 함수를 이용."
+      ],
+      "metadata": {
+        "id": "6xUsx9rqoXj6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ratings = ratings[['userId', 'movieId', 'rating']]\n",
+        "#pivot_table()의 인자로 columns = 'movieId'와 같이 부여하면 movieId 칼럼의 모든 값이 새로운 칼럼 이름으로 변환\n",
+        "ratings_matrix = ratings.pivot_table('rating', index = 'userId', columns ='movieId')\n",
+        "ratings_matrix.head(3)"
+      ],
+      "metadata": {
+        "id": "2XjxD8d0w825",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
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+      "execution_count": 69,
+      "outputs": [
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+            "text/plain": [
+              "movieId  1       2       3       4       5       6       7       8       \\\n",
+              "userId                                                                    \n",
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+              "userId                                   \n",
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+              "\n",
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+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "\n",
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+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ratings_matrix"
+            }
+          },
+          "metadata": {},
+          "execution_count": 69
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* NaN 값이 많음: 사용자가 평점을 매기지 않은 영화가 칼럼으로 변환\n",
+        "* 최소평점이 0 => NaN은 모두 0으로 변환 NaN 값이 많음: 사용자가 평점을 매기지 않은 영화가 칼럼으로 변환\n",
+        "* 최소평점이 0 => NaN은 모두 0으로 변환"
+      ],
+      "metadata": {
+        "id": "TZUOwitLpKH4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#title 칼럼을 얻기 위해 movies와 조인\n",
+        "rating_movies = pd.merge(ratings, movies, on='movieId')\n",
+        "\n",
+        "#columns = 'title'로 title  칼럼으로 피벗 수행\n",
+        "ratings_matrix = rating_movies.pivot_table('rating', index = 'userId', columns='title')\n",
+        "\n",
+        "#NaN을 모두 0으로\n",
+        "ratings_matrix = ratings_matrix.fillna(0)\n",
+        "ratings_matrix.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 345
+        },
+        "id": "ZYUN-yaVpmhf",
+        "outputId": "0a37d271-4a8a-481a-f193-7e440970e7f9"
+      },
+      "execution_count": 70,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
+              "userId                                                        \n",
+              "1              0.0                                      0.0   \n",
+              "2              0.0                                      0.0   \n",
+              "3              0.0                                      0.0   \n",
+              "\n",
+              "title   'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
+              "userId                                                \n",
+              "1                          0.0                  0.0   \n",
+              "2                          0.0                  0.0   \n",
+              "3                          0.0                  0.0   \n",
+              "\n",
+              "title   'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
+              "userId                                                               \n",
+              "1                             0.0                              0.0   \n",
+              "2                             0.0                              0.0   \n",
+              "3                             0.0                              0.0   \n",
+              "\n",
+              "title   'burbs, The (1989)  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
+              "userId                                                                          \n",
+              "1                      0.0                   0.0                          0.0   \n",
+              "2                      0.0                   0.0                          0.0   \n",
+              "3                      0.0                   0.0                          0.0   \n",
+              "\n",
+              "title   *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
+              "userId                                  ...                              \n",
+              "1                                  0.0  ...          0.0           0.0   \n",
+              "2                                  0.0  ...          0.0           0.0   \n",
+              "3                                  0.0  ...          0.0           0.0   \n",
+              "\n",
+              "title   [REC]² (2009)  [REC]³ 3 Génesis (2012)  \\\n",
+              "userId                                           \n",
+              "1                 0.0                      0.0   \n",
+              "2                 0.0                      0.0   \n",
+              "3                 0.0                      0.0   \n",
+              "\n",
+              "title   anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
+              "userId                                                           \n",
+              "1                                                          0.0   \n",
+              "2                                                          0.0   \n",
+              "3                                                          0.0   \n",
+              "\n",
+              "title   eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
+              "userId                                                                \n",
+              "1                   0.0         0.0                             0.0   \n",
+              "2                   0.0         0.0                             0.0   \n",
+              "3                   0.0         0.0                             0.0   \n",
+              "\n",
+              "title   ¡Three Amigos! (1986)  À nous la liberté (Freedom for Us) (1931)  \n",
+              "userId                                                                    \n",
+              "1                         4.0                                        0.0  \n",
+              "2                         0.0                                        0.0  \n",
+              "3                         0.0                                        0.0  \n",
+              "\n",
+              "[3 rows x 9719 columns]"
+            ],
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+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>title</th>\n",
+              "      <th>'71 (2014)</th>\n",
+              "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
+              "      <th>'Round Midnight (1986)</th>\n",
+              "      <th>'Salem's Lot (2004)</th>\n",
+              "      <th>'Til There Was You (1997)</th>\n",
+              "      <th>'Tis the Season for Love (2015)</th>\n",
+              "      <th>'burbs, The (1989)</th>\n",
+              "      <th>'night Mother (1986)</th>\n",
+              "      <th>(500) Days of Summer (2009)</th>\n",
+              "      <th>*batteries not included (1987)</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Zulu (2013)</th>\n",
+              "      <th>[REC] (2007)</th>\n",
+              "      <th>[REC]² (2009)</th>\n",
+              "      <th>[REC]³ 3 Génesis (2012)</th>\n",
+              "      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n",
+              "      <th>eXistenZ (1999)</th>\n",
+              "      <th>xXx (2002)</th>\n",
+              "      <th>xXx: State of the Union (2005)</th>\n",
+              "      <th>¡Three Amigos! (1986)</th>\n",
+              "      <th>À nous la liberté (Freedom for Us) (1931)</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>userId</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
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+              "      <th></th>\n",
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+              "      <th></th>\n",
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+              "      <th></th>\n",
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+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
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+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 9719 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da4867f6-330d-4841-9f5d-e6777a858baa')\"\n",
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+              "            style=\"display:none;\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
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+              "      background-color: #E8F0FE;\n",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
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+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
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+              "      fill: #174EA6;\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "    <script>\n",
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+              "        document.querySelector('#df-da4867f6-330d-4841-9f5d-e6777a858baa button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-da4867f6-330d-4841-9f5d-e6777a858baa');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
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+              "\n",
+              "\n",
+              "    <div id=\"df-df16e4b5-e74a-4df3-b089-c5363e03f98a\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-df16e4b5-e74a-4df3-b089-c5363e03f98a')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
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+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
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+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
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+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-df16e4b5-e74a-4df3-b089-c5363e03f98a button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ratings_matrix"
+            }
+          },
+          "metadata": {},
+          "execution_count": 70
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**영화 간 유사도 산출**\n",
+        "* cosine_similarity()는 행 간의 유사도 산출 but, 위 데이터에서는 영화가 아닌 userID간의 유사도가 산출됨"
+      ],
+      "metadata": {
+        "id": "-B-F7w8Fq0lk"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ratings_matrix_T = ratings_matrix.transpose()\n",
+        "ratings_matrix_T.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "id": "sI7bKBD2raXy",
+        "outputId": "822147d9-a3d3-4a63-cf3a-4a1f1c913389"
+      },
+      "execution_count": 71,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "userId                                   1    2    3    4    5    6    7    \\\n",
+              "title                                                                        \n",
+              "'71 (2014)                               0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
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+              "\n",
+              "userId                                   8    9    10   ...  601  602  603  \\\n",
+              "title                                                   ...                  \n",
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+              "\n",
+              "userId                                   604  605  606  607  608  609  610  \n",
+              "title                                                                       \n",
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+              "'Round Midnight (1986)                   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
+              "\n",
+              "[3 rows x 610 columns]"
+            ],
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+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>'Round Midnight (1986)</th>\n",
+              "      <td>0.0</td>\n",
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+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 610 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-35af2546-b5a2-415b-807f-b1852e36f8d0')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-35af2546-b5a2-415b-807f-b1852e36f8d0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-35af2546-b5a2-415b-807f-b1852e36f8d0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-365208b5-ff4f-4b21-aeaa-cf69d8b538f4\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-365208b5-ff4f-4b21-aeaa-cf69d8b538f4')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "    <g>\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-365208b5-ff4f-4b21-aeaa-cf69d8b538f4 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ratings_matrix_T"
+            }
+          },
+          "metadata": {},
+          "execution_count": 71
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics.pairwise import cosine_similarity\n",
+        "\n",
+        "item_sim = cosine_similarity(ratings_matrix_T, ratings_matrix_T)\n",
+        "\n",
+        "item_sim_df = pd.DataFrame(data = item_sim, index = ratings_matrix.columns,\n",
+        "                           columns = ratings_matrix.columns)\n",
+        "print(item_sim_df.shape)\n",
+        "item_sim_df.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 485
+        },
+        "id": "LLg_HOABvdQQ",
+        "outputId": "e8e6b703-f0a8-4ed9-937a-207d0e4bd2d1"
+      },
+      "execution_count": 72,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(9719, 9719)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title                                    '71 (2014)  \\\n",
+              "title                                                 \n",
+              "'71 (2014)                                      1.0   \n",
+              "'Hellboy': The Seeds of Creation (2004)         0.0   \n",
+              "'Round Midnight (1986)                          0.0   \n",
+              "\n",
+              "title                                    'Hellboy': The Seeds of Creation (2004)  \\\n",
+              "title                                                                              \n",
+              "'71 (2014)                                                              0.000000   \n",
+              "'Hellboy': The Seeds of Creation (2004)                                 1.000000   \n",
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+              "title                                    'Round Midnight (1986)  \\\n",
+              "title                                                             \n",
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+              "'Round Midnight (1986)                                 1.000000   \n",
+              "\n",
+              "title                                    'Salem's Lot (2004)  \\\n",
+              "title                                                          \n",
+              "'71 (2014)                                               0.0   \n",
+              "'Hellboy': The Seeds of Creation (2004)                  0.0   \n",
+              "'Round Midnight (1986)                                   0.0   \n",
+              "\n",
+              "title                                    'Til There Was You (1997)  \\\n",
+              "title                                                                \n",
+              "'71 (2014)                                                     0.0   \n",
+              "'Hellboy': The Seeds of Creation (2004)                        0.0   \n",
+              "'Round Midnight (1986)                                         0.0   \n",
+              "\n",
+              "title                                    'Tis the Season for Love (2015)  \\\n",
+              "title                                                                      \n",
+              "'71 (2014)                                                           0.0   \n",
+              "'Hellboy': The Seeds of Creation (2004)                              0.0   \n",
+              "'Round Midnight (1986)                                               0.0   \n",
+              "\n",
+              "title                                    'burbs, The (1989)  \\\n",
+              "title                                                         \n",
+              "'71 (2014)                                         0.000000   \n",
+              "'Hellboy': The Seeds of Creation (2004)            0.000000   \n",
+              "'Round Midnight (1986)                             0.176777   \n",
+              "\n",
+              "title                                    'night Mother (1986)  \\\n",
+              "title                                                           \n",
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+              "'Round Midnight (1986)                                    0.0   \n",
+              "\n",
+              "title                                    (500) Days of Summer (2009)  \\\n",
+              "title                                                                  \n",
+              "'71 (2014)                                                  0.141653   \n",
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+              "\n",
+              "title                                    *batteries not included (1987)  ...  \\\n",
+              "title                                                                    ...   \n",
+              "'71 (2014)                                                          0.0  ...   \n",
+              "'Hellboy': The Seeds of Creation (2004)                             0.0  ...   \n",
+              "'Round Midnight (1986)                                              0.0  ...   \n",
+              "\n",
+              "title                                    Zulu (2013)  [REC] (2007)  \\\n",
+              "title                                                                \n",
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+              "'Round Midnight (1986)                           0.0      0.000000   \n",
+              "\n",
+              "title                                    [REC]² (2009)  \\\n",
+              "title                                                    \n",
+              "'71 (2014)                                    0.543305   \n",
+              "'Hellboy': The Seeds of Creation (2004)       0.000000   \n",
+              "'Round Midnight (1986)                        0.000000   \n",
+              "\n",
+              "title                                    [REC]³ 3 Génesis (2012)  \\\n",
+              "title                                                              \n",
+              "'71 (2014)                                              0.707107   \n",
+              "'Hellboy': The Seeds of Creation (2004)                 0.000000   \n",
+              "'Round Midnight (1986)                                  0.000000   \n",
+              "\n",
+              "title                                    anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
+              "title                                                                                             \n",
+              "'71 (2014)                                                                                  0.0   \n",
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+              "'Round Midnight (1986)                                                                      0.0   \n",
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+              "title                                                                  \n",
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+              "'Round Midnight (1986)                               0.0    0.000000   \n",
+              "\n",
+              "title                                    xXx: State of the Union (2005)  \\\n",
+              "title                                                                     \n",
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+              "'Round Midnight (1986)                                         0.000000   \n",
+              "\n",
+              "title                                    ¡Three Amigos! (1986)  \\\n",
+              "title                                                            \n",
+              "'71 (2014)                                                 0.0   \n",
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+              "title                                                                               \n",
+              "'71 (2014)                                                                     0.0  \n",
+              "'Hellboy': The Seeds of Creation (2004)                                        0.0  \n",
+              "'Round Midnight (1986)                                                         0.0  \n",
+              "\n",
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+            ],
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+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.342055</td>\n",
+              "      <td>0.543305</td>\n",
+              "      <td>0.707107</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.139431</td>\n",
+              "      <td>0.327327</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.707107</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>'Round Midnight (1986)</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.707107</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.176777</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 9719 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fbd067e8-4ff7-41e0-8228-98bc2808bfd6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-fbd067e8-4ff7-41e0-8228-98bc2808bfd6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-fbd067e8-4ff7-41e0-8228-98bc2808bfd6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-14308b4d-80f5-4ad8-b553-718a6de36eae\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-14308b4d-80f5-4ad8-b553-718a6de36eae')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-14308b4d-80f5-4ad8-b553-718a6de36eae button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "item_sim_df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 72
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "item_sim_df[\"Godfather, The (1972)\"].sort_values(ascending=False)[:6]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 303
+        },
+        "id": "U2H_-HrYwSVw",
+        "outputId": "2c47443b-6130-49bd-807a-c963f98142c3"
+      },
+      "execution_count": 73,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title\n",
+              "Godfather, The (1972)                        1.000000\n",
+              "Godfather: Part II, The (1974)               0.821773\n",
+              "Goodfellas (1990)                            0.664841\n",
+              "One Flew Over the Cuckoo's Nest (1975)       0.620536\n",
+              "Star Wars: Episode IV - A New Hope (1977)    0.595317\n",
+              "Fargo (1996)                                 0.588614\n",
+              "Name: Godfather, The (1972), dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Godfather, The (1972)</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>title</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Godfather, The (1972)</th>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Godfather: Part II, The (1974)</th>\n",
+              "      <td>0.821773</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Goodfellas (1990)</th>\n",
+              "      <td>0.664841</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>One Flew Over the Cuckoo's Nest (1975)</th>\n",
+              "      <td>0.620536</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Star Wars: Episode IV - A New Hope (1977)</th>\n",
+              "      <td>0.595317</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Fargo (1996)</th>\n",
+              "      <td>0.588614</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 73
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "item_sim_df[\"Inception (2010)\"].sort_values(ascending=False)[1:6]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 272
+        },
+        "id": "6WdgQhjiwmjR",
+        "outputId": "de8bc4db-3e05-4448-e802-f4e36e952326"
+      },
+      "execution_count": 74,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title\n",
+              "Dark Knight, The (2008)          0.727263\n",
+              "Inglourious Basterds (2009)      0.646103\n",
+              "Shutter Island (2010)            0.617736\n",
+              "Dark Knight Rises, The (2012)    0.617504\n",
+              "Fight Club (1999)                0.615417\n",
+              "Name: Inception (2010), dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Inception (2010)</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>title</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Dark Knight, The (2008)</th>\n",
+              "      <td>0.727263</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Inglourious Basterds (2009)</th>\n",
+              "      <td>0.646103</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Shutter Island (2010)</th>\n",
+              "      <td>0.617736</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Dark Knight Rises, The (2012)</th>\n",
+              "      <td>0.617504</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Fight Club (1999)</th>\n",
+              "      <td>0.615417</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 74
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**아이템 기반 최근접 이웃 협업 필터링으로 개인화된 영화 추천**\n",
+        "![화면 캡처 2025-12-24 005955.png](data:image/png;base64,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)"
+      ],
+      "metadata": {
+        "id": "7kVnf14sw3_v"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "![화면 캡처 2025-12-24 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)"
+      ],
+      "metadata": {
+        "id": "5fiwPSG48Nr7"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* N값은 아이템의 최근접 이웃 범위 계수를 의미-> 특정 아이템과 유사도가 가장 높은 Top-N개의 다른 아이템을 추출하는데 사용"
+      ],
+      "metadata": {
+        "id": "Pb7HZydc8e-i"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def predict_rating(ratings_arr, item_sim_arr):\n",
+        "  ratings_pred = ratings_arr.dot(item_sim_arr)/np.array([np.abs(item_sim_arr).sum(axis = 1)])\n",
+        "  return ratings_pred"
+      ],
+      "metadata": {
+        "id": "Gm7XQcyY9I86"
+      },
+      "execution_count": 75,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#예측 평점\n",
+        "ratings_pred = predict_rating(ratings_matrix.values, item_sim_df.values)\n",
+        "ratings_pred_matrix = pd.DataFrame(data=ratings_pred, index = ratings_matrix.index,\n",
+        "                                   columns = ratings_matrix.columns)\n",
+        "ratings_pred_matrix.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 345
+        },
+        "id": "aaHeQJkeqzS0",
+        "outputId": "2fa188c1-dfb2-4c35-bd3c-22ff73304a77"
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
+              "userId                                                        \n",
+              "1         0.070345                                 0.577855   \n",
+              "2         0.018260                                 0.042744   \n",
+              "3         0.011884                                 0.030279   \n",
+              "\n",
+              "title   'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
+              "userId                                                \n",
+              "1                     0.321696             0.227055   \n",
+              "2                     0.018861             0.000000   \n",
+              "3                     0.064437             0.003762   \n",
+              "\n",
+              "title   'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
+              "userId                                                               \n",
+              "1                        0.206958                         0.194615   \n",
+              "2                        0.000000                         0.035995   \n",
+              "3                        0.003749                         0.002722   \n",
+              "\n",
+              "title   'burbs, The (1989)  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
+              "userId                                                                          \n",
+              "1                 0.249883              0.102542                     0.157084   \n",
+              "2                 0.013413              0.002314                     0.032213   \n",
+              "3                 0.014625              0.002085                     0.005666   \n",
+              "\n",
+              "title   *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
+              "userId                                  ...                              \n",
+              "1                             0.178197  ...     0.113608      0.181738   \n",
+              "2                             0.014863  ...     0.015640      0.020855   \n",
+              "3                             0.006272  ...     0.006923      0.011665   \n",
+              "\n",
+              "title   [REC]² (2009)  [REC]³ 3 Génesis (2012)  \\\n",
+              "userId                                           \n",
+              "1            0.133962                 0.128574   \n",
+              "2            0.020119                 0.015745   \n",
+              "3            0.011800                 0.012225   \n",
+              "\n",
+              "title   anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
+              "userId                                                           \n",
+              "1                                                     0.006179   \n",
+              "2                                                     0.049983   \n",
+              "3                                                     0.000000   \n",
+              "\n",
+              "title   eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
+              "userId                                                                \n",
+              "1              0.212070    0.192921                        0.136024   \n",
+              "2              0.014876    0.021616                        0.024528   \n",
+              "3              0.008194    0.007017                        0.009229   \n",
+              "\n",
+              "title   ¡Three Amigos! (1986)  À nous la liberté (Freedom for Us) (1931)  \n",
+              "userId                                                                    \n",
+              "1                    0.292955                                   0.720347  \n",
+              "2                    0.017563                                   0.000000  \n",
+              "3                    0.010420                                   0.084501  \n",
+              "\n",
+              "[3 rows x 9719 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6dbe4d7f-5e44-4f7b-84a7-5986c75359a0\" class=\"colab-df-container\">\n",
+              "    <div>\n",
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+              "        vertical-align: middle;\n",
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+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>title</th>\n",
+              "      <th>'71 (2014)</th>\n",
+              "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
+              "      <th>'Round Midnight (1986)</th>\n",
+              "      <th>'Salem's Lot (2004)</th>\n",
+              "      <th>'Til There Was You (1997)</th>\n",
+              "      <th>'Tis the Season for Love (2015)</th>\n",
+              "      <th>'burbs, The (1989)</th>\n",
+              "      <th>'night Mother (1986)</th>\n",
+              "      <th>(500) Days of Summer (2009)</th>\n",
+              "      <th>*batteries not included (1987)</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Zulu (2013)</th>\n",
+              "      <th>[REC] (2007)</th>\n",
+              "      <th>[REC]² (2009)</th>\n",
+              "      <th>[REC]³ 3 Génesis (2012)</th>\n",
+              "      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n",
+              "      <th>eXistenZ (1999)</th>\n",
+              "      <th>xXx (2002)</th>\n",
+              "      <th>xXx: State of the Union (2005)</th>\n",
+              "      <th>¡Three Amigos! (1986)</th>\n",
+              "      <th>À nous la liberté (Freedom for Us) (1931)</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>userId</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.070345</td>\n",
+              "      <td>0.577855</td>\n",
+              "      <td>0.321696</td>\n",
+              "      <td>0.227055</td>\n",
+              "      <td>0.206958</td>\n",
+              "      <td>0.194615</td>\n",
+              "      <td>0.249883</td>\n",
+              "      <td>0.102542</td>\n",
+              "      <td>0.157084</td>\n",
+              "      <td>0.178197</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.113608</td>\n",
+              "      <td>0.181738</td>\n",
+              "      <td>0.133962</td>\n",
+              "      <td>0.128574</td>\n",
+              "      <td>0.006179</td>\n",
+              "      <td>0.212070</td>\n",
+              "      <td>0.192921</td>\n",
+              "      <td>0.136024</td>\n",
+              "      <td>0.292955</td>\n",
+              "      <td>0.720347</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.018260</td>\n",
+              "      <td>0.042744</td>\n",
+              "      <td>0.018861</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.035995</td>\n",
+              "      <td>0.013413</td>\n",
+              "      <td>0.002314</td>\n",
+              "      <td>0.032213</td>\n",
+              "      <td>0.014863</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.015640</td>\n",
+              "      <td>0.020855</td>\n",
+              "      <td>0.020119</td>\n",
+              "      <td>0.015745</td>\n",
+              "      <td>0.049983</td>\n",
+              "      <td>0.014876</td>\n",
+              "      <td>0.021616</td>\n",
+              "      <td>0.024528</td>\n",
+              "      <td>0.017563</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.011884</td>\n",
+              "      <td>0.030279</td>\n",
+              "      <td>0.064437</td>\n",
+              "      <td>0.003762</td>\n",
+              "      <td>0.003749</td>\n",
+              "      <td>0.002722</td>\n",
+              "      <td>0.014625</td>\n",
+              "      <td>0.002085</td>\n",
+              "      <td>0.005666</td>\n",
+              "      <td>0.006272</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.006923</td>\n",
+              "      <td>0.011665</td>\n",
+              "      <td>0.011800</td>\n",
+              "      <td>0.012225</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.008194</td>\n",
+              "      <td>0.007017</td>\n",
+              "      <td>0.009229</td>\n",
+              "      <td>0.010420</td>\n",
+              "      <td>0.084501</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 9719 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6dbe4d7f-5e44-4f7b-84a7-5986c75359a0')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-6dbe4d7f-5e44-4f7b-84a7-5986c75359a0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-6dbe4d7f-5e44-4f7b-84a7-5986c75359a0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-655a0de1-ea1d-44bc-b625-0cb8c81f2a3c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-655a0de1-ea1d-44bc-b625-0cb8c81f2a3c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "    <g>\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-655a0de1-ea1d-44bc-b625-0cb8c81f2a3c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ratings_pred_matrix"
+            }
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#실제 평점과의 차이\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "def get_mse(pred, actual):\n",
+        "  pred = pred[actual.nonzero()].flatten()\n",
+        "  actual = actual[actual.nonzero()].flatten()\n",
+        "  return mean_squared_error(pred, actual)\n",
+        "\n",
+        "print('아이템 기반 모든 최근접 이웃 MSE:', get_mse(ratings_pred, ratings_matrix.values))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "svZOAsty--90",
+        "outputId": "583a9cbf-755d-48d8-a5fd-4aeea4baf766"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "아이템 기반 모든 최근접 이웃 MSE: 9.895354759094706\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def predict_rating_topsim(ratings_arr, item_sim_arr, n = 20):\n",
+        "  pred = np.zeros(ratings_arr.shape)\n",
+        "\n",
+        "  for col in range(ratings_arr.shape[1]):\n",
+        "    top_n_items = [np.argsort(item_sim_arr[:, col])[:-n-1:-1]]\n",
+        "    for row in range(ratings_arr.shape[0]):\n",
+        "      pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row,:][top_n_items].T)\n",
+        "      pred[row, col] /= np.sum(np.abs(item_sim_arr[col, :][top_n_items]))\n",
+        "\n",
+        "  return pred"
+      ],
+      "metadata": {
+        "id": "8oTvBsktAPNK"
+      },
+      "execution_count": 37,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ratings_pred = predict_rating_topsim(ratings_matrix.values, item_sim_df.values, n = 20)\n",
+        "print('아이템 기반 최근접 Top-20 이웃 MSE: ', get_mse(ratings_pred, ratings_matrix.values))\n",
+        "\n",
+        "ratings_pred_matrix = pd.DataFrame(data = ratings_pred, index = ratings_matrix.index, columns= ratings_matrix.columns)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zb1_ffRyBFeq",
+        "outputId": "41ec29db-b0f4-4653-abc9-7cff42c04294"
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "아이템 기반 최근접 Top-20 이웃 MSE:  3.6949827608772314\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "user_rating_id = ratings_matrix.loc[9, :]\n",
+        "user_rating_id[user_rating_id >0].sort_values(ascending=False)[:10]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 429
+        },
+        "id": "R5Bqv4mJBhiD",
+        "outputId": "2cc475d9-f9ba-4bbb-a486-55a022bf0d71"
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title\n",
+              "Adaptation (2002)                                                                 5.0\n",
+              "Citizen Kane (1941)                                                               5.0\n",
+              "Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)    5.0\n",
+              "Producers, The (1968)                                                             5.0\n",
+              "Lord of the Rings: The Two Towers, The (2002)                                     5.0\n",
+              "Lord of the Rings: The Fellowship of the Ring, The (2001)                         5.0\n",
+              "Back to the Future (1985)                                                         5.0\n",
+              "Austin Powers in Goldmember (2002)                                                5.0\n",
+              "Minority Report (2002)                                                            4.0\n",
+              "Witness (1985)                                                                    4.0\n",
+              "Name: 9, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>9</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>title</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Adaptation (2002)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Citizen Kane (1941)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Producers, The (1968)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Lord of the Rings: The Two Towers, The (2002)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Lord of the Rings: The Fellowship of the Ring, The (2001)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Back to the Future (1985)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Austin Powers in Goldmember (2002)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Minority Report (2002)</th>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Witness (1985)</th>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 39
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_unseen_movies(ratings_matrix, userId):\n",
+        "  user_rating = ratings_matrix.loc[userId, :]\n",
+        "  already_seen = user_rating[user_rating >0].index.tolist()\n",
+        "  movies_list = ratings_matrix.columns.tolist()\n",
+        "  unseen_list = [movie for movie in movies_list if movie not in already_seen]\n",
+        "  return unseen_list"
+      ],
+      "metadata": {
+        "id": "Ad--lUP_BvfG"
+      },
+      "execution_count": 40,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def recomm_movie_by_userId(pred_df, userId, unseen_list, top_n=10):\n",
+        "  recomm_movies = pred_df.loc[userId, unseen_list].sort_values(ascending = False)[:top_n]\n",
+        "  return recomm_movies\n",
+        "\n",
+        "unseen_list = get_unseen_movies(ratings_matrix, 9)\n",
+        "\n",
+        "recomm_movies = recomm_movie_by_userId(ratings_pred_matrix, 9, unseen_list, top_n = 10)\n",
+        "\n",
+        "recomm_movies = pd.DataFrame(data = recomm_movies.values, index = recomm_movies.index,\n",
+        "                             columns=['pred_score'])\n",
+        "recomm_movies"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 394
+        },
+        "id": "HynFIaJqCYX5",
+        "outputId": "fa64ce0c-37ea-41ce-f0f7-1970816fc495"
+      },
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                                                                pred_score\n",
+              "title                                                                                                     \n",
+              "Shrek (2001)                                                                                      0.866202\n",
+              "Spider-Man (2002)                                                                                 0.857854\n",
+              "Last Samurai, The (2003)                                                                          0.817473\n",
+              "Indiana Jones and the Temple of Doom (1984)                                                       0.816626\n",
+              "Matrix Reloaded, The (2003)                                                                       0.800990\n",
+              "Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001)    0.765159\n",
+              "Gladiator (2000)                                                                                  0.740956\n",
+              "Matrix, The (1999)                                                                                0.732693\n",
+              "Pirates of the Caribbean: The Curse of the Black Pearl (2003)                                     0.689591\n",
+              "Lord of the Rings: The Return of the King, The (2003)                                             0.676711"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-54010626-d5f6-47f9-8751-f147f5d476d6\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>pred_score</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>title</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Shrek (2001)</th>\n",
+              "      <td>0.866202</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Spider-Man (2002)</th>\n",
+              "      <td>0.857854</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Last Samurai, The (2003)</th>\n",
+              "      <td>0.817473</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Indiana Jones and the Temple of Doom (1984)</th>\n",
+              "      <td>0.816626</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Matrix Reloaded, The (2003)</th>\n",
+              "      <td>0.800990</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001)</th>\n",
+              "      <td>0.765159</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Gladiator (2000)</th>\n",
+              "      <td>0.740956</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Matrix, The (1999)</th>\n",
+              "      <td>0.732693</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Pirates of the Caribbean: The Curse of the Black Pearl (2003)</th>\n",
+              "      <td>0.689591</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Lord of the Rings: The Return of the King, The (2003)</th>\n",
+              "      <td>0.676711</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-54010626-d5f6-47f9-8751-f147f5d476d6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
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+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-54010626-d5f6-47f9-8751-f147f5d476d6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-54010626-d5f6-47f9-8751-f147f5d476d6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
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+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "      --disabled-bg-color: #3B4455;\n",
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+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-1971a7c1-752d-44ef-ad44-0f193ec8da64 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_3b146eef-9bb9-42e3-9b38-839d0e57a72c\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('recomm_movies')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_3b146eef-9bb9-42e3-9b38-839d0e57a72c button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('recomm_movies');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "recomm_movies",
+              "summary": "{\n  \"name\": \"recomm_movies\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Pirates of the Caribbean: The Curse of the Black Pearl (2003)\",\n          \"Spider-Man (2002)\",\n          \"Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pred_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06614432811511851,\n        \"min\": 0.6767108283499336,\n        \"max\": 0.8662018746933645,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.6895905595608812,\n          0.8578535950426878,\n          0.7651586070058114\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**행렬 분해를 이용한 잠재 요인 협업 필터링 실습**\n"
+      ],
+      "metadata": {
+        "id": "UL4MPaLtDKba"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def matrix_factorization(R, K, steps = 200, learning_rate= 0.01, r_lambda = 0.01):\n",
+        "  num_users, num_items = R.shape\n",
+        "  np.random.seed(1)\n",
+        "\n",
+        "  P = np.random.normal(scale = 1./K, size = (num_users, K))\n",
+        "  Q = np.random.normal(scale = 1./K, size = (num_items, K))\n",
+        "\n",
+        "  non_zeros = [(i, j ,R[i,j]) for i in range(num_users) for j in range(num_items) if R[i, j]>0]\n",
+        "  for step in range(steps):\n",
+        "    for i, j, r in non_zeros:\n",
+        "      eij = r-np.dot(P[i, :], Q[j, :].T)\n",
+        "      P[i, :] = P[i,:] + learning_rate*(eij*Q[j, :]-r_lambda*P[i, :])\n",
+        "      Q[j, :] = Q[j,:] + learning_rate*(eij*P[i, :]-r_lambda*Q[j, :])\n",
+        "\n",
+        "    rmse = get_rmse(R, P, Q, non_zeros)\n",
+        "    if(step%10) == 0:\n",
+        "      print('### iteration step:', step, \"rmse:\", rmse)\n",
+        "  return P,Q"
+      ],
+      "metadata": {
+        "id": "SiZFW1s4IJY9"
+      },
+      "execution_count": 42,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "movies = pd.read_csv('/content/movies.csv')\n",
+        "ratings = pd.read_csv('/content/ratings.csv')\n",
+        "ratings = ratings[['userId', 'movieId', 'rating']]\n",
+        "ratings_matrix = ratings.pivot_table('rating', index = 'userId', columns = 'movieId')\n",
+        "\n",
+        "rating_movies = pd.merge(ratings, movies, on = 'movieId')\n",
+        "ratings_matrix = rating_movies.pivot_table('rating', index = 'userId', columns = 'title')"
+      ],
+      "metadata": {
+        "id": "MwdnEOAzJgkw"
+      },
+      "execution_count": 43,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "P, Q = matrix_factorization(ratings_matrix.values, K=50, steps=200, learning_rate=0.01, r_lambda = 0.01)\n",
+        "pred_matrix = np.dot(P, Q.T)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3emxwCD4KIV8",
+        "outputId": "877eacb7-910f-402c-f682-1571df1ec890"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "### iteration step: 0 rmse: None\n",
+            "### iteration step: 10 rmse: None\n",
+            "### iteration step: 20 rmse: None\n",
+            "### iteration step: 30 rmse: None\n",
+            "### iteration step: 40 rmse: None\n",
+            "### iteration step: 50 rmse: None\n",
+            "### iteration step: 60 rmse: None\n",
+            "### iteration step: 70 rmse: None\n",
+            "### iteration step: 80 rmse: None\n",
+            "### iteration step: 90 rmse: None\n",
+            "### iteration step: 100 rmse: None\n",
+            "### iteration step: 110 rmse: None\n",
+            "### iteration step: 120 rmse: None\n",
+            "### iteration step: 130 rmse: None\n",
+            "### iteration step: 140 rmse: None\n",
+            "### iteration step: 150 rmse: None\n",
+            "### iteration step: 160 rmse: None\n",
+            "### iteration step: 170 rmse: None\n",
+            "### iteration step: 180 rmse: None\n",
+            "### iteration step: 190 rmse: None\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ratings_pred_matrix = pd.DataFrame(data=pred_matrix, index= ratings_matrix.index, columns = ratings_matrix.columns)\n",
+        "ratings_pred_matrix.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 345
+        },
+        "id": "tRE-gmHYKQYx",
+        "outputId": "c3ffec10-c3a4-4cb6-c7b8-871d87127040"
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
+              "userId                                                        \n",
+              "1         3.055084                                 4.092018   \n",
+              "2         3.170119                                 3.657992   \n",
+              "3         2.307073                                 1.658853   \n",
+              "\n",
+              "title   'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
+              "userId                                                \n",
+              "1                     3.564130             4.502167   \n",
+              "2                     3.308707             4.166521   \n",
+              "3                     1.443538             2.208859   \n",
+              "\n",
+              "title   'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
+              "userId                                                               \n",
+              "1                        3.981215                         1.271694   \n",
+              "2                        4.311890                         1.275469   \n",
+              "3                        2.229486                         0.780760   \n",
+              "\n",
+              "title   'burbs, The (1989)  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
+              "userId                                                                          \n",
+              "1                 3.603274              2.333266                     5.091749   \n",
+              "2                 4.237972              1.900366                     3.392859   \n",
+              "3                 1.997043              0.924908                     2.970700   \n",
+              "\n",
+              "title   *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
+              "userId                                  ...                              \n",
+              "1                             3.972454  ...     1.402608      4.208382   \n",
+              "2                             3.647421  ...     0.973811      3.528264   \n",
+              "3                             2.551446  ...     0.520354      1.709494   \n",
+              "\n",
+              "title   [REC]² (2009)  [REC]³ 3 Génesis (2012)  \\\n",
+              "userId                                           \n",
+              "1            3.705957                 2.720514   \n",
+              "2            3.361532                 2.672535   \n",
+              "3            2.281596                 1.782833   \n",
+              "\n",
+              "title   anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
+              "userId                                                           \n",
+              "1                                                     2.787331   \n",
+              "2                                                     2.404456   \n",
+              "3                                                     1.635173   \n",
+              "\n",
+              "title   eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
+              "userId                                                                \n",
+              "1              3.475076    3.253458                        2.161087   \n",
+              "2              4.232789    2.911602                        1.634576   \n",
+              "3              1.323276    2.887580                        1.042618   \n",
+              "\n",
+              "title   ¡Three Amigos! (1986)  À nous la liberté (Freedom for Us) (1931)  \n",
+              "userId                                                                    \n",
+              "1                    4.010495                                   0.859474  \n",
+              "2                    4.135735                                   0.725684  \n",
+              "3                    2.293890                                   0.396941  \n",
+              "\n",
+              "[3 rows x 9719 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7f10d34f-ca41-4273-b57e-a57426bb1357\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>title</th>\n",
+              "      <th>'71 (2014)</th>\n",
+              "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
+              "      <th>'Round Midnight (1986)</th>\n",
+              "      <th>'Salem's Lot (2004)</th>\n",
+              "      <th>'Til There Was You (1997)</th>\n",
+              "      <th>'Tis the Season for Love (2015)</th>\n",
+              "      <th>'burbs, The (1989)</th>\n",
+              "      <th>'night Mother (1986)</th>\n",
+              "      <th>(500) Days of Summer (2009)</th>\n",
+              "      <th>*batteries not included (1987)</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Zulu (2013)</th>\n",
+              "      <th>[REC] (2007)</th>\n",
+              "      <th>[REC]² (2009)</th>\n",
+              "      <th>[REC]³ 3 Génesis (2012)</th>\n",
+              "      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n",
+              "      <th>eXistenZ (1999)</th>\n",
+              "      <th>xXx (2002)</th>\n",
+              "      <th>xXx: State of the Union (2005)</th>\n",
+              "      <th>¡Three Amigos! (1986)</th>\n",
+              "      <th>À nous la liberté (Freedom for Us) (1931)</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>userId</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>3.055084</td>\n",
+              "      <td>4.092018</td>\n",
+              "      <td>3.564130</td>\n",
+              "      <td>4.502167</td>\n",
+              "      <td>3.981215</td>\n",
+              "      <td>1.271694</td>\n",
+              "      <td>3.603274</td>\n",
+              "      <td>2.333266</td>\n",
+              "      <td>5.091749</td>\n",
+              "      <td>3.972454</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.402608</td>\n",
+              "      <td>4.208382</td>\n",
+              "      <td>3.705957</td>\n",
+              "      <td>2.720514</td>\n",
+              "      <td>2.787331</td>\n",
+              "      <td>3.475076</td>\n",
+              "      <td>3.253458</td>\n",
+              "      <td>2.161087</td>\n",
+              "      <td>4.010495</td>\n",
+              "      <td>0.859474</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3.170119</td>\n",
+              "      <td>3.657992</td>\n",
+              "      <td>3.308707</td>\n",
+              "      <td>4.166521</td>\n",
+              "      <td>4.311890</td>\n",
+              "      <td>1.275469</td>\n",
+              "      <td>4.237972</td>\n",
+              "      <td>1.900366</td>\n",
+              "      <td>3.392859</td>\n",
+              "      <td>3.647421</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.973811</td>\n",
+              "      <td>3.528264</td>\n",
+              "      <td>3.361532</td>\n",
+              "      <td>2.672535</td>\n",
+              "      <td>2.404456</td>\n",
+              "      <td>4.232789</td>\n",
+              "      <td>2.911602</td>\n",
+              "      <td>1.634576</td>\n",
+              "      <td>4.135735</td>\n",
+              "      <td>0.725684</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2.307073</td>\n",
+              "      <td>1.658853</td>\n",
+              "      <td>1.443538</td>\n",
+              "      <td>2.208859</td>\n",
+              "      <td>2.229486</td>\n",
+              "      <td>0.780760</td>\n",
+              "      <td>1.997043</td>\n",
+              "      <td>0.924908</td>\n",
+              "      <td>2.970700</td>\n",
+              "      <td>2.551446</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.520354</td>\n",
+              "      <td>1.709494</td>\n",
+              "      <td>2.281596</td>\n",
+              "      <td>1.782833</td>\n",
+              "      <td>1.635173</td>\n",
+              "      <td>1.323276</td>\n",
+              "      <td>2.887580</td>\n",
+              "      <td>1.042618</td>\n",
+              "      <td>2.293890</td>\n",
+              "      <td>0.396941</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 9719 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
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+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
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+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7f10d34f-ca41-4273-b57e-a57426bb1357 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7f10d34f-ca41-4273-b57e-a57426bb1357');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
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+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-3675db36-32b5-440e-b3d1-97cf48c0f7ae button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ratings_pred_matrix"
+            }
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 사용자가 관람하지 않은 영화명 추출\n",
+        "unseen_list = get_unseen_movies(ratings_matrix, 9)\n",
+        "# 잠재 요인 협업 필터링으로 영화 추천\n",
+        "recomm_movies = recomm_movie_by_userId(ratings_pred_matrix, 9, unseen_list, top_n=10)\n",
+        "# 평점 데이터를 DataFrame으로 생성.\n",
+        "recomm_movies = pd.DataFrame(data=recomm_movies.values, index=recomm_movies.index, columns=['pred_score'])\n",
+        "recomm_movies"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 394
+        },
+        "id": "D2_UAkzhKei7",
+        "outputId": "fc975360-2ab2-42bb-b03f-1772bf2a5612"
+      },
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                       pred_score\n",
+              "title                                                            \n",
+              "Rear Window (1954)                                       5.704612\n",
+              "South Park: Bigger, Longer and Uncut (1999)              5.451100\n",
+              "Rounders (1998)                                          5.298393\n",
+              "Blade Runner (1982)                                      5.244951\n",
+              "Roger & Me (1989)                                        5.191962\n",
+              "Gattaca (1997)                                           5.183179\n",
+              "Ben-Hur (1959)                                           5.130463\n",
+              "Rosencrantz and Guildenstern Are Dead (1990)             5.087375\n",
+              "Big Lebowski, The (1998)                                 5.038690\n",
+              "Star Wars: Episode V - The Empire Strikes Back (1980)    4.989601"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-71ed48ec-d83f-4821-b6f1-f1f0650feef8\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>pred_score</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>title</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Rear Window (1954)</th>\n",
+              "      <td>5.704612</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>South Park: Bigger, Longer and Uncut (1999)</th>\n",
+              "      <td>5.451100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rounders (1998)</th>\n",
+              "      <td>5.298393</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Blade Runner (1982)</th>\n",
+              "      <td>5.244951</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Roger &amp; Me (1989)</th>\n",
+              "      <td>5.191962</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Gattaca (1997)</th>\n",
+              "      <td>5.183179</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Ben-Hur (1959)</th>\n",
+              "      <td>5.130463</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rosencrantz and Guildenstern Are Dead (1990)</th>\n",
+              "      <td>5.087375</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Big Lebowski, The (1998)</th>\n",
+              "      <td>5.038690</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Star Wars: Episode V - The Empire Strikes Back (1980)</th>\n",
+              "      <td>4.989601</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-71ed48ec-d83f-4821-b6f1-f1f0650feef8')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-71ed48ec-d83f-4821-b6f1-f1f0650feef8 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-71ed48ec-d83f-4821-b6f1-f1f0650feef8');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-ac475a6a-65dd-4fa0-8ed0-4a66cbb85d95\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ac475a6a-65dd-4fa0-8ed0-4a66cbb85d95')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-ac475a6a-65dd-4fa0-8ed0-4a66cbb85d95 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_3e7872be-3f85-4fa8-aa86-813ef0baf505\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('recomm_movies')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_3e7872be-3f85-4fa8-aa86-813ef0baf505 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('recomm_movies');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "recomm_movies",
+              "summary": "{\n  \"name\": \"recomm_movies\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Big Lebowski, The (1998)\",\n          \"South Park: Bigger, Longer and Uncut (1999)\",\n          \"Gattaca (1997)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pred_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21272885538651393,\n        \"min\": 4.989601238872484,\n        \"max\": 5.704612469838172,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          5.0386897288205725,\n          5.451100205772531,\n          5.183178550884765\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 46
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **08.파이썬 추천 시스템 패키지-Surprise**"
+      ],
+      "metadata": {
+        "id": "8yZkAqfbiT78"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install scikit-surprise"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "moXV4rgAiaVQ",
+        "outputId": "6498b906-947f-45f2-bdc7-d47e1a81db6a"
+      },
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: scikit-surprise in /usr/local/lib/python3.12/dist-packages (1.1.4)\n",
+            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-surprise) (1.5.3)\n",
+            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.12/dist-packages (from scikit-surprise) (1.26.4)\n",
+            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from scikit-surprise) (1.16.3)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* Surprise에서 데이터 로딩은 Dataset 클래스를 이용해서만 가능.\n",
+        "* Surprise는 무비렌즈 사이트에서 제공하는 과거 버전의 데이터 세트를 가져오는 API를 제공\n",
+        "* Surprise Dataset 클래스의 load_builtin()은 무비렌즈 사이트에서 제공하는 과거 버전\n",
+        "데이터 세트인 ‘ml-10*(10만 개 평점 데이터) 또는 ‘ml-lm’(100만 개 평점 데이터) 데이터를 아카\n",
+        "이브 사이트로부터 내려받아 로컬 디렉터리에 저장한 뒤 데이터를 로딩합니다."
+      ],
+      "metadata": {
+        "id": "MHg0fV8ulD2t"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from surprise import SVD\n",
+        "from surprise import Dataset\n",
+        "from surprise import accuracy\n",
+        "from surprise.model_selection import train_test_split"
+      ],
+      "metadata": {
+        "id": "DiqAwYlhkZNT"
+      },
+      "execution_count": 48,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data = Dataset.load_builtin('ml-100k')\n",
+        "#수행 시마다 동일하게 데이터를 분할하기 위해 random_state 값 부여\n",
+        "trainset, testset = train_test_split(data, test_size =.25, random_state=0)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "B_Ws32sEqTS9",
+        "outputId": "421d975d-a273-4ccd-8a5b-0e505a2db2e3"
+      },
+      "execution_count": 76,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Dataset ml-100k could not be found. Do you want to download it? [Y/n] Y\n",
+            "Trying to download dataset from https://files.grouplens.org/datasets/movielens/ml-100k.zip...\n",
+            "Done! Dataset ml-100k has been saved to /root/.surprise_data/ml-100k\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* 과거 버전의 데이터 세트. 분리 문자가 \\t문자\n",
+        "* 무비렌즈 사이트에서 내려받은 데이터 파일과 동이랗게 로우 레벨의 사용자-아이템 평점 데이터를 그대로 적용 과거 버전의 데이터 세트. 분리 문자가 \\t문자\n",
+        "* 무비렌즈 사이트에서 내려받은 데이터 파일과 동이랗게 로우 레벨의 사용자-아이템 평점 데이터를 그대로 적용"
+      ],
+      "metadata": {
+        "id": "wwyFrujWqo1b"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "algo = SVD(random_state=0)\n",
+        "algo.fit(trainset)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "urgvZhtwrX1u",
+        "outputId": "fc59dc3e-8d58-4bf1-9dac-83e0360ff754"
+      },
+      "execution_count": 77,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x7a76e39af170>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 77
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "predictions = algo.test(testset)\n",
+        "print('prediction type:', type(predictions), 'size:', len(predictions))\n",
+        "print('prediction 결과의 최초 5개 추출')\n",
+        "predictions[:5]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "z_GMf6ChrpkA",
+        "outputId": "1efa8ca0-a7f4-4322-d275-0f6ca255d67d"
+      },
+      "execution_count": 78,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "prediction type: <class 'list'> size: 25000\n",
+            "prediction 결과의 최초 5개 추출\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[Prediction(uid='120', iid='282', r_ui=4.0, est=3.5114147666251547, details={'was_impossible': False}),\n",
+              " Prediction(uid='882', iid='291', r_ui=4.0, est=3.573872419581491, details={'was_impossible': False}),\n",
+              " Prediction(uid='535', iid='507', r_ui=5.0, est=4.033583485472447, details={'was_impossible': False}),\n",
+              " Prediction(uid='697', iid='244', r_ui=5.0, est=3.8463639495936905, details={'was_impossible': False}),\n",
+              " Prediction(uid='751', iid='385', r_ui=4.0, est=3.1807542478219157, details={'was_impossible': False})]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 78
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "[(pred.uid, pred.iid, pred.est) for pred in predictions[:3]]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "L8vU9v2VsYxG",
+        "outputId": "9ec028ae-c219-46c9-a3cc-e542e0410cc4"
+      },
+      "execution_count": 79,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[('120', '282', 3.5114147666251547),\n",
+              " ('882', '291', 3.573872419581491),\n",
+              " ('535', '507', 4.033583485472447)]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 79
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "uid = str(196)\n",
+        "iid = str(302)\n",
+        "pred = algo.predict(uid, iid)\n",
+        "print(pred)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3LXfM9KfsenL",
+        "outputId": "5bd249db-4e4f-4634-b2f0-c3476031a094"
+      },
+      "execution_count": 80,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "user: 196        item: 302        r_ui = None   est = 4.49   {'was_impossible': False}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "accuracy.rmse(predictions)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZVUmMYoos1HB",
+        "outputId": "064ee1b1-3fc1-419c-ed03-2ed0d35e27d1"
+      },
+      "execution_count": 81,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 0.9467\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9466860806937948"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 81
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**OS 파일 데이터를 Surprise 데이터 세트로 로딩**\n",
+        "* 주의: 로딩되는 데이터 파일에 칼럼명을 가지는 헤더 문자열이 있어서는 안됨. -> ratings.csv 파일은 헤더를 가지고 있음.->to_csv() 함수로 헤더 삭제"
+      ],
+      "metadata": {
+        "id": "pB5LFb3bs5zF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "ratings = pd.read_csv('/content/ratings.csv')\n",
+        "#헤더 제거한 새로운 파일 생성\n",
+        "ratings.to_csv('/content/ratings_noh.csv', index = False, header=False)"
+      ],
+      "metadata": {
+        "id": "9K0qLK5DudQ1"
+      },
+      "execution_count": 95,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* Reader 클래스를 이용해 데이터 파일의 파싱 포맷을 정의\n",
+        "* 4개의 칼럼이 사용자 아이디, 아이템 아이디, 평점, 타임스탬프임을 로딩할 때 알려줘야함"
+      ],
+      "metadata": {
+        "id": "YJC8vh3luzzr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from surprise import Reader\n",
+        "\n",
+        "reader = Reader(line_format='user item rating timestamp', sep = ',', rating_scale = (0.5,5))\n",
+        "data = Dataset.load_from_file('/content/ratings_noh.csv', reader=reader)"
+      ],
+      "metadata": {
+        "id": "G4JqDZCcvC7Z"
+      },
+      "execution_count": 96,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* Surprise 데이터 세트는 기본적으로 무비렌즈 데이터 형식을 따르므로 무비렌즈 데이터 형식이 아닌\n",
+        "다른 OS 파일의 경우 Reader 클래스를 먼저 설정\n",
+        "**Reader 클래스 주요 파라미터**\n",
+        "* line_format(string): 칼럼을 순서대로 나열. 문자열을 공백으로 분리\n",
+        "* sep(char): 칼럼을 분리하는 분리자. 디폴트: \\t. 판다스에서 입력받을 경우 기재 필요X\n",
+        "* rating_scale(tuple, optional): 평점 값의 최소~최대 평점 설저. 디폴트: (1,5)"
+      ],
+      "metadata": {
+        "id": "P1oYHVz9wQKj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trainset, testset = train_test_split(data, test_size =.25, random_state=0)\n",
+        "\n",
+        "algo = SVD(n_factors = 50, random_state=0)\n",
+        "algo.fit(trainset)\n",
+        "predictions = algo.test(testset)\n",
+        "accuracy.rmse(predictions)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HIxK1R0TxDli",
+        "outputId": "53b1e56f-4476-4a59-b7bf-76195f174b60"
+      },
+      "execution_count": 97,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 0.8682\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8681952927143516"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 97
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "** 판다스 DataFrame에서 Surprise 데이터 세트로 로딩**"
+      ],
+      "metadata": {
+        "id": "2yTvebbex2Ix"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "from surprise import Reader, Dataset\n",
+        "\n",
+        "ratings = pd.read_csv('/content/ratings.csv')\n",
+        "reader = Reader(rating_scale=(0.5, 5.0))\n",
+        "\n",
+        "data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']], reader)\n",
+        "trainset, testset = train_test_split(data, test_size = .25, random_state =0)\n",
+        "\n",
+        "algo = SVD(n_factors = 50, random_state=0)\n",
+        "algo.fit(trainset)\n",
+        "predictions = algo.test(testset)\n",
+        "accuracy.rmse(predictions)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "T4k_EmaPx56x",
+        "outputId": "98635611-b126-4a34-c525-f1006567d9ec"
+      },
+      "execution_count": 98,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 0.8682\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8681952927143516"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 98
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**교차 검증과 하이퍼 파라미터 튜닝**\n",
+        "* Surprise는 교차 검증과 하이퍼 파라미터 튜닝을 위해 사이킷런과 유사한 cross_validate( )와 GridSearchCV 클래스를 제공"
+      ],
+      "metadata": {
+        "id": "iLmPV9Mt29JF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from surprise.model_selection import cross_validate\n",
+        "\n",
+        "ratings = pd.read_csv('/content/ratings.csv')\n",
+        "reader = Reader(rating_scale = (0.5, 5.0))\n",
+        "data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']], reader)\n",
+        "\n",
+        "algo = SVD(random_state = 0)\n",
+        "cross_validate(algo, data, measures=['RMSE', 'MAE'], cv = 5, verbose= True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "R5HFRwSg3Fz7",
+        "outputId": "3e34caee-f2b0-49bd-eef8-15bcc76f43e8"
+      },
+      "execution_count": 100,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Evaluating RMSE, MAE of algorithm SVD on 5 split(s).\n",
+            "\n",
+            "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
+            "RMSE (testset)    0.8739  0.8639  0.8700  0.8787  0.8775  0.8728  0.0054  \n",
+            "MAE (testset)     0.6704  0.6644  0.6710  0.6747  0.6739  0.6709  0.0036  \n",
+            "Fit time          2.36    1.69    1.51    1.63    1.86    1.81    0.30    \n",
+            "Test time         0.20    0.11    0.11    0.13    0.19    0.15    0.04    \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'test_rmse': array([0.87386097, 0.86385394, 0.87001019, 0.87869206, 0.87754842]),\n",
+              " 'test_mae': array([0.67040961, 0.66442237, 0.67098542, 0.67468529, 0.67385167]),\n",
+              " 'fit_time': (2.3593761920928955,\n",
+              "  1.6873219013214111,\n",
+              "  1.5088114738464355,\n",
+              "  1.625060796737671,\n",
+              "  1.8643643856048584),\n",
+              " 'test_time': (0.19586944580078125,\n",
+              "  0.11407184600830078,\n",
+              "  0.11287569999694824,\n",
+              "  0.12824273109436035,\n",
+              "  0.19462895393371582)}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 100
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from surprise.model_selection import GridSearchCV\n",
+        "\n",
+        "param_grid = {'n_epochs': [20, 40,60], 'n_factors':[50, 100, 200]}\n",
+        "gs = GridSearchCV(SVD, param_grid, measures=['rmse', 'mae'], cv=3)\n",
+        "gs.fit(data)\n",
+        "\n",
+        "print(gs.best_score['rmse'])\n",
+        "print(gs.best_params['rmse'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eq5jlRby37on",
+        "outputId": "9c873c46-8033-46e5-8fb9-2f28b89a66df"
+      },
+      "execution_count": 102,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.8779338881055662\n",
+            "{'n_epochs': 20, 'n_factors': 50}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Surprise를 이용한 개인화 영화 추천 시스템 구축**"
+      ],
+      "metadata": {
+        "id": "9PQ4ss695v_D"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#다음 코드는 train_test_split()으로 분리되지 않은 데이터 세트에 fit()을 호출해 오류가 발생\n",
+        "data = Dataset.load_from_df(ratings['userId', 'movieId', 'rating'], reader)\n",
+        "algo = SVD(n_factors = 50, random_state=0)\n",
+        "algo.fit(data)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 569
+        },
+        "id": "Qp0Q5TPI6Dgq",
+        "outputId": "000b165e-226e-4943-a516-c84ea91985df"
+      },
+      "execution_count": 103,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "KeyError",
+          "evalue": "('userId', 'movieId', 'rating')",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3805\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3806\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;31mKeyError\u001b[0m: ('userId', 'movieId', 'rating')",
+            "\nThe above exception was the direct cause of the following exception:\n",
+            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-3089583357.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#다음 코드는 train_test_split()으로 분리되지 않은 데이터 세트에 fit()을 호출해 오류가 발생\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_from_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mratings\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'userId'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'movieId'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rating'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0malgo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSVD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_factors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0malgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4101\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4102\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4103\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4104\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3810\u001b[0m             ):\n\u001b[1;32m   3811\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3812\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3813\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3814\u001b[0m             \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyError\u001b[0m: ('userId', 'movieId', 'rating')"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#DatasetAutoFolds 클래스\n",
+        "from surprise.dataset import DatasetAutoFolds\n",
+        "\n",
+        "reader = Reader(line_format='user item rating timestamp', sep = ',', rating_scale = (0.5,5))\n",
+        "#DatasetAutoFolds 클래스를 ratings_noh.csv 파일 기반으로 생성\n",
+        "data_folds = DatasetAutoFolds(ratings_file = '/content/ratings_noh.csv', reader = reader)\n",
+        "\n",
+        "#전체 데이터를 학습 데이터로 생성\n",
+        "trainset = data_folds.build_full_trainset()"
+      ],
+      "metadata": {
+        "id": "TuxvxeSp6Xhr"
+      },
+      "execution_count": 104,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "algo = SVD(n_epochs=20, n_factors = 50, random_state=0)\n",
+        "algo.fit(trainset)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "D7t3MOdeuHzf",
+        "outputId": "0c0f45bf-a3eb-47cf-ebdf-bcf3b819e790"
+      },
+      "execution_count": 105,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x7a7692fd9940>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 105
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "movies = pd.read_csv('/content/movies.csv')\n",
+        "\n",
+        "movieIds = ratings[ratings['userId'] == 9]['movieId']\n",
+        "\n",
+        "if movieIds[movieIds==42].count() ==0:\n",
+        "  print('사용자 아이디 9는 영화 아이디 42의 평점 없음')\n",
+        "\n",
+        "print(movies[movies['movieId']==42])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "93GKv6Td7EmI",
+        "outputId": "f3389d01-7eba-4dd9-ab07-a2b7b9c46155"
+      },
+      "execution_count": 108,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "사용자 아이디 9는 영화 아이디 42의 평점 없음\n",
+            "    movieId                   title              genres\n",
+            "38       42  Dead Presidents (1995)  Action|Crime|Drama\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "uid = str(9)\n",
+        "iid = str(42)\n",
+        "\n",
+        "pred = algo.predict(uid, iid, verbose=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "a0ps2XQ77hAG",
+        "outputId": "dce1d789-13bc-450e-9848-baf9d47faf04"
+      },
+      "execution_count": 109,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "user: 9          item: 42         r_ui = None   est = 3.13   {'was_impossible': False}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_unseen_surprise(ratings, movies, userId):\n",
+        "  seen_movies = ratings[ratings['userId']==userId]['movieId'].tolist()\n",
+        "  total_movies = movies['movieId'].tolist()\n",
+        "\n",
+        "  unseen_movies = [movie for movie in total_movies if movie not in movies]\n",
+        "  print('평점 매긴 영화 수:', len(seen_movies), '추천 대상 영화 수:', len(unseen_movies),\n",
+        "        '전체 영화 수:', len(total_movies))\n",
+        "  return unseen_movies\n",
+        "\n",
+        "unseen_movies = get_unseen_surprise(ratings, movies, 9)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SVL_7m017o_u",
+        "outputId": "b502a190-dc8d-41ab-dffe-75ca1c2b5bcb"
+      },
+      "execution_count": 111,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "평점 매긴 영화 수: 46 추천 대상 영화 수: 9742 전체 영화 수: 9742\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def recomm_movie_by_surprise(algo, userId, unseen_movies, top_n=10):\n",
+        "  predictions = [algo.predict(str(userId), str(movieId)) for movieId in unseen_movies]\n",
+        "\n",
+        "  def sortkey_est(pred):\n",
+        "    return pred.est\n",
+        "  predictions.sort(key=sortkey_est, reverse=True)\n",
+        "  top_predictions = predictions[:top_n]\n",
+        "\n",
+        "  top_movie_ids = [int(pred.iid) for pred in top_predictions]\n",
+        "  top_movie_rating = [pred.est for pred in top_predictions]\n",
+        "  top_movie_titles = movies[movies.movieId.isin(top_movie_ids)]['title']\n",
+        "  top_movie_preds = [(id, title, rating) for id, title, rating in zip(top_movie_ids, top_movie_titles, top_movie_rating)]\n",
+        "\n",
+        "  return top_movie_preds\n",
+        "\n",
+        "unseen_movies = get_unseen_surprise(ratings, movies, 9)\n",
+        "top_movie_preds = recomm_movie_by_surprise(algo, 9, unseen_movies, top_n=10)\n",
+        "\n",
+        "print('#### Top-10 추천 영화 리스트 ####')\n",
+        "for top_movie in top_movie_preds:\n",
+        "  print(top_movie[1], \":\", top_movie[2])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ed_wXo3I8z6A",
+        "outputId": "b119bf8f-2239-497f-db87-75aa1409bbce"
+      },
+      "execution_count": 112,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "평점 매긴 영화 수: 46 추천 대상 영화 수: 9742 전체 영화 수: 9742\n",
+            "#### Top-10 추천 영화 리스트 ####\n",
+            "Usual Suspects, The (1995) : 4.306302135700814\n",
+            "Star Wars: Episode IV - A New Hope (1977) : 4.281663842987387\n",
+            "Pulp Fiction (1994) : 4.278152632122759\n",
+            "Godfather, The (1972) : 4.226073566460876\n",
+            "Streetcar Named Desire, A (1951) : 4.205267497432363\n",
+            "Star Wars: Episode V - The Empire Strikes Back (1980) : 4.1918097904381995\n",
+            "Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981) : 4.154746591122657\n",
+            "Star Wars: Episode VI - Return of the Jedi (1983) : 4.122016128534504\n",
+            "Goodfellas (1990) : 4.118002684813024\n",
+            "Lord of the Rings: The Fellowship of the Ring, The (2001) : 4.108009609093436\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\354\235\264\354\213\234\355\230\204.pdf" "b/Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\354\235\264\354\213\234\355\230\204.pdf"
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HcmV?d00001


From 277e644aac4b22ac4fa6a17b7030544032362c7b Mon Sep 17 00:00:00 2001
From: Greatharmony <133874863+Greatharmony@users.noreply.github.com>
Date: Sun, 28 Dec 2025 00:15:23 +0900
Subject: [PATCH 2/2] =?UTF-8?q?Week16=5F=EB=B3=B5=EC=8A=B5=EA=B3=BC?=
 =?UTF-8?q?=EC=A0=9C=5F=EC=9D=B4=EC=8B=9C=ED=98=84?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...354\235\264\354\213\234\355\230\204.ipynb" | 6981 +++++++++++++++++
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 create mode 100644 "Week16_\353\263\265\354\212\265\352\263\274\354\240\234_\354\235\264\354\213\234\355\230\204.ipynb"

diff --git "a/Week16_\353\263\265\354\212\265\352\263\274\354\240\234_\354\235\264\354\213\234\355\230\204.ipynb" "b/Week16_\353\263\265\354\212\265\352\263\274\354\240\234_\354\235\264\354\213\234\355\230\204.ipynb"
new file mode 100644
index 0000000..1cef1c1
--- /dev/null
+++ "b/Week16_\353\263\265\354\212\265\352\263\274\354\240\234_\354\235\264\354\213\234\355\230\204.ipynb"
@@ -0,0 +1,6981 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **05. 콘텐츠 기반 필터링 실습-TMDB 5000 영화 데이터 세트**\n",
+        "\n",
+        "**장르 속성을 이용한 영화 콘텐츠 기반 필터링**\n",
+        "* 특정 영화와 비슷한 특성/속성, 구성 요소 등을 가진 다른 영화 추천\n",
+        "* 영화 간의 유사성을 판단하는 기준이 영화를 구성하는 다양한 콘텐츠를 기반으로 하는 방식\n",
+        "\n",
+        "**데이터 로딩 및 가공**"
+      ],
+      "metadata": {
+        "id": "h7poBF8WG7ng"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import warnings; warnings.filterwarnings('ignore')\n",
+        "\n",
+        "movies = pd.read_csv('/content/tmdb_5000_movies.csv')\n",
+        "print(movies.shape)\n",
+        "movies.head(1)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 327
+        },
+        "id": "KILZrMfNH3oN",
+        "outputId": "d3db7976-21ff-4195-a69f-06a6687fff95"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(4803, 20)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      budget  \\\n",
+              "0  237000000   \n",
+              "\n",
+              "                                                                                                genres  \\\n",
+              "0  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {...   \n",
+              "\n",
+              "                      homepage     id  \\\n",
+              "0  http://www.avatarmovie.com/  19995   \n",
+              "\n",
+              "                                                                                              keywords  \\\n",
+              "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\": 2964, \"name\": \"future\"}, {\"id\": 3386, \"name\": \"sp...   \n",
+              "\n",
+              "  original_language original_title  \\\n",
+              "0                en         Avatar   \n",
+              "\n",
+              "                                                                                              overview  \\\n",
+              "0  In the 22nd century, a paraplegic Marine is dispatched to the moon Pandora on a unique mission, ...   \n",
+              "\n",
+              "   popularity  \\\n",
+              "0  150.437577   \n",
+              "\n",
+              "                                                                                  production_companies  \\\n",
+              "0  [{\"name\": \"Ingenious Film Partners\", \"id\": 289}, {\"name\": \"Twentieth Century Fox Film Corporatio...   \n",
+              "\n",
+              "                                                                                  production_countries  \\\n",
+              "0  [{\"iso_3166_1\": \"US\", \"name\": \"United States of America\"}, {\"iso_3166_1\": \"GB\", \"name\": \"United ...   \n",
+              "\n",
+              "  release_date     revenue  runtime  \\\n",
+              "0   2009-12-10  2787965087    162.0   \n",
+              "\n",
+              "                                                                        spoken_languages  \\\n",
+              "0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso_639_1\": \"es\", \"name\": \"Espa\\u00f1ol\"}]   \n",
+              "\n",
+              "     status                      tagline   title  vote_average  vote_count  \n",
+              "0  Released  Enter the World of Pandora.  Avatar           7.2       11800  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4cebf5f5-747d-403d-a986-2646c1b84987\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>budget</th>\n",
+              "      <th>genres</th>\n",
+              "      <th>homepage</th>\n",
+              "      <th>id</th>\n",
+              "      <th>keywords</th>\n",
+              "      <th>original_language</th>\n",
+              "      <th>original_title</th>\n",
+              "      <th>overview</th>\n",
+              "      <th>popularity</th>\n",
+              "      <th>production_companies</th>\n",
+              "      <th>production_countries</th>\n",
+              "      <th>release_date</th>\n",
+              "      <th>revenue</th>\n",
+              "      <th>runtime</th>\n",
+              "      <th>spoken_languages</th>\n",
+              "      <th>status</th>\n",
+              "      <th>tagline</th>\n",
+              "      <th>title</th>\n",
+              "      <th>vote_average</th>\n",
+              "      <th>vote_count</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>237000000</td>\n",
+              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {...</td>\n",
+              "      <td>http://www.avatarmovie.com/</td>\n",
+              "      <td>19995</td>\n",
+              "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\": 2964, \"name\": \"future\"}, {\"id\": 3386, \"name\": \"sp...</td>\n",
+              "      <td>en</td>\n",
+              "      <td>Avatar</td>\n",
+              "      <td>In the 22nd century, a paraplegic Marine is dispatched to the moon Pandora on a unique mission, ...</td>\n",
+              "      <td>150.437577</td>\n",
+              "      <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289}, {\"name\": \"Twentieth Century Fox Film Corporatio...</td>\n",
+              "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States of America\"}, {\"iso_3166_1\": \"GB\", \"name\": \"United ...</td>\n",
+              "      <td>2009-12-10</td>\n",
+              "      <td>2787965087</td>\n",
+              "      <td>162.0</td>\n",
+              "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso_639_1\": \"es\", \"name\": \"Espa\\u00f1ol\"}]</td>\n",
+              "      <td>Released</td>\n",
+              "      <td>Enter the World of Pandora.</td>\n",
+              "      <td>Avatar</td>\n",
+              "      <td>7.2</td>\n",
+              "      <td>11800</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4cebf5f5-747d-403d-a986-2646c1b84987')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4cebf5f5-747d-403d-a986-2646c1b84987 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4cebf5f5-747d-403d-a986-2646c1b84987');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "movies",
+              "summary": "{\n  \"name\": \"movies\",\n  \"rows\": 4803,\n  \"fields\": [\n    {\n      \"column\": \"budget\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40722391,\n        \"min\": 0,\n        \"max\": 380000000,\n        \"num_unique_values\": 436,\n        \"samples\": [\n          439000,\n          68000000,\n          700000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1175,\n        \"samples\": [\n          \"[{\\\"id\\\": 14, \\\"name\\\": \\\"Fantasy\\\"}, {\\\"id\\\": 12, \\\"name\\\": \\\"Adventure\\\"}, {\\\"id\\\": 16, \\\"name\\\": \\\"Animation\\\"}]\",\n          \"[{\\\"id\\\": 28, \\\"name\\\": \\\"Action\\\"}, {\\\"id\\\": 35, \\\"name\\\": \\\"Comedy\\\"}, {\\\"id\\\": 80, \\\"name\\\": \\\"Crime\\\"}, {\\\"id\\\": 18, \\\"name\\\": \\\"Drama\\\"}]\",\n          \"[{\\\"id\\\": 12, \\\"name\\\": \\\"Adventure\\\"}, {\\\"id\\\": 16, \\\"name\\\": \\\"Animation\\\"}, {\\\"id\\\": 10751, \\\"name\\\": \\\"Family\\\"}, {\\\"id\\\": 14, \\\"name\\\": \\\"Fantasy\\\"}, {\\\"id\\\": 878, \\\"name\\\": \\\"Science Fiction\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"homepage\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1691,\n        \"samples\": [\n          \"https://www.warnerbros.com/running-scared\",\n          \"http://www.51birchstreet.com/index.php\",\n          \"http://movies2.foxjapan.com/glee/\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88694,\n        \"min\": 5,\n        \"max\": 459488,\n        \"num_unique_values\": 4803,\n        \"samples\": [\n          8427,\n          13006,\n          18041\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keywords\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4222,\n        \"samples\": [\n          \"[{\\\"id\\\": 782, \\\"name\\\": \\\"assassin\\\"}, {\\\"id\\\": 1872, \\\"name\\\": \\\"loss of father\\\"}, {\\\"id\\\": 2908, \\\"name\\\": \\\"secret society\\\"}, {\\\"id\\\": 3045, \\\"name\\\": \\\"mission of murder\\\"}, {\\\"id\\\": 9748, \\\"name\\\": \\\"revenge\\\"}]\",\n          \"[{\\\"id\\\": 2987, \\\"name\\\": \\\"gang war\\\"}, {\\\"id\\\": 4942, \\\"name\\\": \\\"victim of murder\\\"}, {\\\"id\\\": 5332, \\\"name\\\": \\\"greed\\\"}, {\\\"id\\\": 6062, \\\"name\\\": \\\"hostility\\\"}, {\\\"id\\\": 156212, \\\"name\\\": \\\"spaghetti western\\\"}]\",\n          \"[{\\\"id\\\": 703, \\\"name\\\": \\\"detective\\\"}, {\\\"id\\\": 1299, \\\"name\\\": \\\"monster\\\"}, {\\\"id\\\": 6101, \\\"name\\\": \\\"engine\\\"}, {\\\"id\\\": 10988, \\\"name\\\": \\\"based on tv series\\\"}, {\\\"id\\\": 15162, \\\"name\\\": \\\"dog\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_language\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 37,\n        \"samples\": [\n          \"xx\",\n          \"ta\",\n          \"es\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4801,\n        \"samples\": [\n          \"I Spy\",\n          \"Love Letters\",\n          \"Sleepover\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"overview\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4800,\n        \"samples\": [\n          \"When the Switchblade, the most sophisticated prototype stealth fighter created yet, is stolen from the U.S. government, one of the United States' top spies, Alex Scott, is called to action. What he doesn't expect is to get teamed up with a cocky civilian, World Class Boxing Champion Kelly Robinson, on a dangerous top secret espionage mission. Their assignment: using equal parts skill and humor, catch Arnold Gundars, one of the world's most successful arms dealers.\",\n          \"When \\\"street smart\\\" rapper Christopher \\\"C-Note\\\" Hawkins (Big Boi) applies for a membership to all-white Carolina Pines Country Club, the establishment's proprietors are hardly ready to oblige him.\",\n          \"As their first year of high school looms ahead, best friends Julie, Hannah, Yancy and Farrah have one last summer sleepover. Little do they know they're about to embark on the adventure of a lifetime. Desperate to shed their nerdy status, they take part in a night-long scavenger hunt that pits them against their popular archrivals. Everything under the sun goes on -- from taking Yancy's father's car to sneaking into nightclubs!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"popularity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31.816649749537806,\n        \"min\": 0.0,\n        \"max\": 875.581305,\n        \"num_unique_values\": 4802,\n        \"samples\": [\n          13.267631,\n          0.010909,\n          5.842299\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"production_companies\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3697,\n        \"samples\": [\n          \"[{\\\"name\\\": \\\"Paramount Pictures\\\", \\\"id\\\": 4}, {\\\"name\\\": \\\"Cherry Alley Productions\\\", \\\"id\\\": 2232}]\",\n          \"[{\\\"name\\\": \\\"Twentieth Century Fox Film Corporation\\\", \\\"id\\\": 306}, {\\\"name\\\": \\\"Dune Entertainment\\\", \\\"id\\\": 444}, {\\\"name\\\": \\\"Regency Enterprises\\\", \\\"id\\\": 508}, {\\\"name\\\": \\\"Guy Walks into a Bar Productions\\\", \\\"id\\\": 2645}, {\\\"name\\\": \\\"Deep River Productions\\\", \\\"id\\\": 2646}, {\\\"name\\\": \\\"Friendly Films (II)\\\", \\\"id\\\": 81136}]\",\n          \"[{\\\"name\\\": \\\"Twentieth Century Fox Film Corporation\\\", \\\"id\\\": 306}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"production_countries\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 469,\n        \"samples\": [\n          \"[{\\\"iso_3166_1\\\": \\\"ES\\\", \\\"name\\\": \\\"Spain\\\"}, {\\\"iso_3166_1\\\": \\\"GB\\\", \\\"name\\\": \\\"United Kingdom\\\"}, {\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}, {\\\"iso_3166_1\\\": \\\"FR\\\", \\\"name\\\": \\\"France\\\"}]\",\n          \"[{\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}, {\\\"iso_3166_1\\\": \\\"CA\\\", \\\"name\\\": \\\"Canada\\\"}, {\\\"iso_3166_1\\\": \\\"DE\\\", \\\"name\\\": \\\"Germany\\\"}]\",\n          \"[{\\\"iso_3166_1\\\": \\\"DE\\\", \\\"name\\\": \\\"Germany\\\"}, {\\\"iso_3166_1\\\": \\\"ES\\\", \\\"name\\\": \\\"Spain\\\"}, {\\\"iso_3166_1\\\": \\\"GB\\\", \\\"name\\\": \\\"United Kingdom\\\"}, {\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"release_date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 3280,\n        \"samples\": [\n          \"1966-10-16\",\n          \"1987-07-31\",\n          \"1993-09-23\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"revenue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 162857100,\n        \"min\": 0,\n        \"max\": 2787965087,\n        \"num_unique_values\": 3297,\n        \"samples\": [\n          11833696,\n          10462500,\n          17807569\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"runtime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.611934588844207,\n        \"min\": 0.0,\n        \"max\": 338.0,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          74.0,\n          85.0,\n          170.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spoken_languages\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 544,\n        \"samples\": [\n          \"[{\\\"iso_639_1\\\": \\\"es\\\", \\\"name\\\": \\\"Espa\\\\u00f1ol\\\"}, {\\\"iso_639_1\\\": \\\"en\\\", \\\"name\\\": \\\"English\\\"}, {\\\"iso_639_1\\\": \\\"fr\\\", \\\"name\\\": \\\"Fran\\\\u00e7ais\\\"}, {\\\"iso_639_1\\\": \\\"hu\\\", \\\"name\\\": \\\"Magyar\\\"}]\",\n          \"[{\\\"iso_639_1\\\": \\\"en\\\", \\\"name\\\": \\\"English\\\"}, {\\\"iso_639_1\\\": \\\"it\\\", \\\"name\\\": \\\"Italiano\\\"}, {\\\"iso_639_1\\\": \\\"pt\\\", \\\"name\\\": \\\"Portugu\\\\u00eas\\\"}]\",\n          \"[{\\\"iso_639_1\\\": \\\"de\\\", \\\"name\\\": \\\"Deutsch\\\"}, {\\\"iso_639_1\\\": \\\"it\\\", \\\"name\\\": \\\"Italiano\\\"}, {\\\"iso_639_1\\\": \\\"la\\\", \\\"name\\\": \\\"Latin\\\"}, {\\\"iso_639_1\\\": \\\"pl\\\", \\\"name\\\": \\\"Polski\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Released\",\n          \"Post Production\",\n          \"Rumored\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tagline\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3944,\n        \"samples\": [\n          \"When you're 17, every day is war.\",\n          \"An Unspeakable Horror. A Creative Genius. Captured For Eternity.\",\n          \"May the schwartz be with you\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4800,\n        \"samples\": [\n          \"I Spy\",\n          \"Who's Your Caddy?\",\n          \"Sleepover\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1946121628478925,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          5.1,\n          7.2,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1234,\n        \"min\": 0,\n        \"max\": 13752,\n        \"num_unique_values\": 1609,\n        \"samples\": [\n          7604,\n          3428,\n          225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 31
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "movies_df = movies[['id', 'title', 'genres', 'vote_average', 'vote_count', 'popularity', 'keywords', 'overview']]"
+      ],
+      "metadata": {
+        "id": "5ElyMDBcIMde"
+      },
+      "execution_count": 32,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* ‘genres’, ‘keywords’ 등과 같은 칼럼을 보면 [{\"id\"： 28, \"name\"： \"Action\"}, {\"id\"： 12, \"name\"：\n",
+        "\"Adventure\"}]와 같이 파이썬 리스트(list) 내부에 여러 개의 딕셔너리(diet)가 있는 형태의 문자열로\n",
+        "표기돼 있음"
+      ],
+      "metadata": {
+        "id": "b11_PCmcMqko"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pd.set_option('max_colwidth', 100)\n",
+        "movies_df[['genres', 'keywords']][:1]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81
+        },
+        "id": "hiMmTFFkNDCX",
+        "outputId": "23adf098-b8a5-4253-eb45-170b4087863f"
+      },
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                                                                genres  \\\n",
+              "0  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {...   \n",
+              "\n",
+              "                                                                                              keywords  \n",
+              "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\": 2964, \"name\": \"future\"}, {\"id\": 3386, \"name\": \"sp...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a13bc3f7-47ad-4bcb-951d-b38b3b86161a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>genres</th>\n",
+              "      <th>keywords</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {...</td>\n",
+              "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\": 2964, \"name\": \"future\"}, {\"id\": 3386, \"name\": \"sp...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a13bc3f7-47ad-4bcb-951d-b38b3b86161a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a13bc3f7-47ad-4bcb-951d-b38b3b86161a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a13bc3f7-47ad-4bcb-951d-b38b3b86161a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"movies_df[['genres', 'keywords']][:1]\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"[{\\\"id\\\": 28, \\\"name\\\": \\\"Action\\\"}, {\\\"id\\\": 12, \\\"name\\\": \\\"Adventure\\\"}, {\\\"id\\\": 14, \\\"name\\\": \\\"Fantasy\\\"}, {\\\"id\\\": 878, \\\"name\\\": \\\"Science Fiction\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keywords\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"[{\\\"id\\\": 1463, \\\"name\\\": \\\"culture clash\\\"}, {\\\"id\\\": 2964, \\\"name\\\": \\\"future\\\"}, {\\\"id\\\": 3386, \\\"name\\\": \\\"space war\\\"}, {\\\"id\\\": 3388, \\\"name\\\": \\\"space colony\\\"}, {\\\"id\\\": 3679, \\\"name\\\": \\\"society\\\"}, {\\\"id\\\": 3801, \\\"name\\\": \\\"space travel\\\"}, {\\\"id\\\": 9685, \\\"name\\\": \\\"futuristic\\\"}, {\\\"id\\\": 9840, \\\"name\\\": \\\"romance\\\"}, {\\\"id\\\": 9882, \\\"name\\\": \\\"space\\\"}, {\\\"id\\\": 9951, \\\"name\\\": \\\"alien\\\"}, {\\\"id\\\": 10148, \\\"name\\\": \\\"tribe\\\"}, {\\\"id\\\": 10158, \\\"name\\\": \\\"alien planet\\\"}, {\\\"id\\\": 10987, \\\"name\\\": \\\"cgi\\\"}, {\\\"id\\\": 11399, \\\"name\\\": \\\"marine\\\"}, {\\\"id\\\": 13065, \\\"name\\\": \\\"soldier\\\"}, {\\\"id\\\": 14643, \\\"name\\\": \\\"battle\\\"}, {\\\"id\\\": 14720, \\\"name\\\": \\\"love affair\\\"}, {\\\"id\\\": 165431, \\\"name\\\": \\\"anti war\\\"}, {\\\"id\\\": 193554, \\\"name\\\": \\\"power relations\\\"}, {\\\"id\\\": 206690, \\\"name\\\": \\\"mind and soul\\\"}, {\\\"id\\\": 209714, \\\"name\\\": \\\"3d\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from ast import literal_eval\n",
+        "import numpy as np\n",
+        "\n",
+        "def safe_literal_eval(val):\n",
+        "    if isinstance(val, str):\n",
+        "        try:\n",
+        "            return literal_eval(val)\n",
+        "        except (ValueError, SyntaxError):\n",
+        "            return []\n",
+        "    elif val is np.nan:\n",
+        "        return []\n",
+        "    else:\n",
+        "        return []\n",
+        "\n",
+        "movies_df['genres'] = movies_df['genres'].apply(safe_literal_eval)\n",
+        "movies_df['keywords'] = movies_df['keywords'].apply(safe_literal_eval)"
+      ],
+      "metadata": {
+        "id": "BbiyDYBFcnrf"
+      },
+      "execution_count": 34,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "movies_df['genres'] = movies_df['genres'].apply(lambda x: [y['name'] for y in x])\n",
+        "movies_df['keywords'] = movies_df['keywords'].apply(lambda x: [y['name'] for y in x])\n",
+        "movies_df[['genres', 'keywords']][:1]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81
+        },
+        "id": "xTnEmykJebTh",
+        "outputId": "181e222c-44fe-4760-edb2-316eb84252d8"
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                          genres  \\\n",
+              "0  [Action, Adventure, Fantasy, Science Fiction]   \n",
+              "\n",
+              "                                                                                              keywords  \n",
+              "0  [culture clash, future, space war, space colony, society, space travel, futuristic, romance, spa...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fd674610-0cd3-40ed-bd95-032934fab905\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>genres</th>\n",
+              "      <th>keywords</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
+              "      <td>[culture clash, future, space war, space colony, society, space travel, futuristic, romance, spa...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fd674610-0cd3-40ed-bd95-032934fab905')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-fd674610-0cd3-40ed-bd95-032934fab905 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-fd674610-0cd3-40ed-bd95-032934fab905');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"movies_df[['genres', 'keywords']][:1]\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keywords\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**장르 콘텐츠 유사도 측정**\n",
+        "* 문자열로 변환된 genres 칼럼을 Count 기반으로 피처 벡터화 변환\n",
+        "* genres 문자열을 피처 벡터화 행렬로 변환한 데이터 세트로 코사인 유사도를 통해 비교합니다. 이를 위해 데이터 세트의 레크드별로 타 레코드와 장르에서 코사인 유사도 값을 가지는 객체를 생성\n",
+        "* 장르 유사도가 높은 영화 중에 평점이 높은 순으로 영화를 추천"
+      ],
+      "metadata": {
+        "id": "BKDGHUopfZf3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.feature_extraction.text import CountVectorizer\n",
+        "\n",
+        "movies_df['genres_literal'] = movies_df['genres'].apply(lambda x:(' ').join(x))\n",
+        "count_vect = CountVectorizer(min_df = 1, ngram_range=(1,2))\n",
+        "genre_mat = count_vect.fit_transform(movies_df['genres_literal'])\n",
+        "print(genre_mat.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bTjG5FRJgcEZ",
+        "outputId": "8797f611-216e-41f4-c1a2-dfa6c3f0b6cc"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(4803, 276)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "![화면 캡처 2025-12-23 183002.png](data:image/png;base64,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)"
+      ],
+      "metadata": {
+        "id": "oLDUmAkAi2dt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics.pairwise import cosine_similarity\n",
+        "\n",
+        "genre_sim = cosine_similarity(genre_mat, genre_mat)\n",
+        "print(genre_sim.shape)\n",
+        "print(genre_sim[:2])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gd4dp9hRi4rR",
+        "outputId": "13211c95-46d8-4f37-e6bf-46cde96924b1"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(4803, 4803)\n",
+            "[[1.         0.59628479 0.4472136  ... 0.         0.         0.        ]\n",
+            " [0.59628479 1.         0.4        ... 0.         0.         0.        ]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "genre_sim_sorted_ind = genre_sim.argsort()[:, ::-1]\n",
+        "print(genre_sim_sorted_ind[:1])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "v7bPtl5ljscw",
+        "outputId": "079d023a-d209-48f2-c4c0-c6d5b41dd75b"
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[   0   46 3494 ... 3331 3333 2031]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**장르 콘텐츠 필터링을 이용한 영화추천**"
+      ],
+      "metadata": {
+        "id": "cX1SHNLcnUSM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def find_sim_movie(df, sorted_ind, title_name, top_n = 10):\n",
+        "  title_movie = df[df['title'] == title_name]\n",
+        "\n",
+        "  title_index = title_movie.index.values\n",
+        "  similar_indexes = sorted_ind[title_index, :(top_n)]\n",
+        "\n",
+        "  print(similar_indexes)\n",
+        "  similar_indexes = similar_indexes.reshape(-1)\n",
+        "\n",
+        "  return df.iloc[similar_indexes]"
+      ],
+      "metadata": {
+        "id": "EwMpgfulnXQR"
+      },
+      "execution_count": 39,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "similar_movies = find_sim_movie(movies_df, genre_sim_sorted_ind, 'The Godfather', 10)\n",
+        "similar_movies[['title', 'vote_average']]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 381
+        },
+        "id": "gFbJliblnw-a",
+        "outputId": "387d11dc-6bd1-47fe-ffb8-e0e9f0f4f6ac"
+      },
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[1881 3378 3866 1370 1464  588 3887 3594 2839  892]]\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                title  vote_average\n",
+              "1881         The Shawshank Redemption           8.5\n",
+              "3378                       Auto Focus           6.1\n",
+              "3866                      City of God           8.1\n",
+              "1370                               21           6.5\n",
+              "1464              Black Water Transit           0.0\n",
+              "588   Wall Street: Money Never Sleeps           5.8\n",
+              "3887                    Trainspotting           7.8\n",
+              "3594                  Spring Breakers           5.0\n",
+              "2839                         Rounders           6.9\n",
+              "892                            Casino           7.8"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0d130f33-faf9-462f-ad12-371bcd8f0fd8\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>title</th>\n",
+              "      <th>vote_average</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1881</th>\n",
+              "      <td>The Shawshank Redemption</td>\n",
+              "      <td>8.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3378</th>\n",
+              "      <td>Auto Focus</td>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3866</th>\n",
+              "      <td>City of God</td>\n",
+              "      <td>8.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1370</th>\n",
+              "      <td>21</td>\n",
+              "      <td>6.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1464</th>\n",
+              "      <td>Black Water Transit</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>588</th>\n",
+              "      <td>Wall Street: Money Never Sleeps</td>\n",
+              "      <td>5.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3887</th>\n",
+              "      <td>Trainspotting</td>\n",
+              "      <td>7.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3594</th>\n",
+              "      <td>Spring Breakers</td>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2839</th>\n",
+              "      <td>Rounders</td>\n",
+              "      <td>6.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>892</th>\n",
+              "      <td>Casino</td>\n",
+              "      <td>7.8</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d130f33-faf9-462f-ad12-371bcd8f0fd8')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-0d130f33-faf9-462f-ad12-371bcd8f0fd8 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0d130f33-faf9-462f-ad12-371bcd8f0fd8');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-822a15b1-6a3d-4986-a8a6-2bdd16a7c9c0\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-822a15b1-6a3d-4986-a8a6-2bdd16a7c9c0')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-822a15b1-6a3d-4986-a8a6-2bdd16a7c9c0 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"similar_movies[['title', 'vote_average']]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Rounders\",\n          \"Auto Focus\",\n          \"Wall Street: Money Never Sleeps\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4636242498490803,\n        \"min\": 0.0,\n        \"max\": 8.5,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          5.0,\n          6.1,\n          5.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 40
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "movies_df[['title', 'vote_average', 'vote_count']].sort_values('vote_average', ascending=False)[:10]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "id": "NoHMGc3Iqdpu",
+        "outputId": "854d8884-74ac-4e8d-f76d-13b37f5c1bbb"
+      },
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                         title  vote_average  vote_count\n",
+              "4662            Little Big Top          10.0           1\n",
+              "3519          Stiff Upper Lips          10.0           1\n",
+              "4045     Dancer, Texas Pop. 81          10.0           1\n",
+              "4247     Me You and Five Bucks          10.0           2\n",
+              "3992                 Sardaarji           9.5           2\n",
+              "2386            One Man's Hero           9.3           2\n",
+              "1881  The Shawshank Redemption           8.5        8205\n",
+              "2970        There Goes My Baby           8.5           2\n",
+              "3337             The Godfather           8.4        5893\n",
+              "2796     The Prisoner of Zenda           8.4          11"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7d1e6074-fd00-43af-9d39-f763ac8a4c93\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>title</th>\n",
+              "      <th>vote_average</th>\n",
+              "      <th>vote_count</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>4662</th>\n",
+              "      <td>Little Big Top</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3519</th>\n",
+              "      <td>Stiff Upper Lips</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4045</th>\n",
+              "      <td>Dancer, Texas Pop. 81</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4247</th>\n",
+              "      <td>Me You and Five Bucks</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3992</th>\n",
+              "      <td>Sardaarji</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2386</th>\n",
+              "      <td>One Man's Hero</td>\n",
+              "      <td>9.3</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1881</th>\n",
+              "      <td>The Shawshank Redemption</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>8205</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2970</th>\n",
+              "      <td>There Goes My Baby</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3337</th>\n",
+              "      <td>The Godfather</td>\n",
+              "      <td>8.4</td>\n",
+              "      <td>5893</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2796</th>\n",
+              "      <td>The Prisoner of Zenda</td>\n",
+              "      <td>8.4</td>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d1e6074-fd00-43af-9d39-f763ac8a4c93')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7d1e6074-fd00-43af-9d39-f763ac8a4c93 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7d1e6074-fd00-43af-9d39-f763ac8a4c93');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-f2066fbc-8fab-4261-a801-07f1ec5dec1c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f2066fbc-8fab-4261-a801-07f1ec5dec1c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-f2066fbc-8fab-4261-a801-07f1ec5dec1c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"movies_df[['title', 'vote_average', 'vote_count']]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"The Godfather\",\n          \"Stiff Upper Lips\",\n          \"One Man's Hero\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7366591251499343,\n        \"min\": 8.4,\n        \"max\": 10.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9.5,\n          8.4,\n          9.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3020,\n        \"min\": 1,\n        \"max\": 8205,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          11,\n          8205\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* 왜곡된 평점 데이터를 회피할 수 있도록 평점에 평가 횟수를 반영할 수 있는 새로운 평가 방식이 필요\n",
+        "\n",
+        "\n",
+        "```\n",
+        "가중평점(Weighted Rating) = (v/(v+m))*R +(m/(v+m))*C\n",
+        "```\n",
+        "* v: 개별 영화에 평점을 투표한 횟수: vote_count\n",
+        "* m: 평점을 부여하기 위한 최소 투표 횟수\n",
+        "* R: 개별 영화에 대한 평균 평점: vote_average\n",
+        "* C: 젼체 영화에 대한 평균 평점\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "MNXToqXeq3Hk"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "C = movies_df['vote_average'].mean()\n",
+        "m = movies_df['vote_count'].quantile(0.6)\n",
+        "print('C:',  round(C,3), 'm:', round(m,3))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Gp4RH5-IrVqD",
+        "outputId": "f300ced6-5e0d-41fb-c7d9-52dd9af737c9"
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "C: 6.092 m: 370.2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "percentile = 0.6\n",
+        "m = movies_df['vote_count'].quantile(percentile)\n",
+        "C = movies_df['vote_average'].mean()\n",
+        "\n",
+        "def weighted_vote_average(record):\n",
+        "  v = record['vote_count']\n",
+        "  R = record['vote_average']\n",
+        "\n",
+        "  return ((v/(v+m))*R) + ((m/(m+v))*C)\n",
+        "\n",
+        "movies_df['weighted_vote'] = movies.apply(weighted_vote_average, axis =1)"
+      ],
+      "metadata": {
+        "id": "CL3Q42bPrsJP"
+      },
+      "execution_count": 43,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "movies_df[['title', 'vote_average', 'weighted_vote', 'vote_count']].sort_values('weighted_vote', ascending = False)[:10]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "id": "Ac2BxbuXsLU1",
+        "outputId": "c73e4b1f-7aad-4224-9ce0-6e1cfbcb717e"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                         title  vote_average  weighted_vote  vote_count\n",
+              "1881  The Shawshank Redemption           8.5       8.396052        8205\n",
+              "3337             The Godfather           8.4       8.263591        5893\n",
+              "662                 Fight Club           8.3       8.216455        9413\n",
+              "3232              Pulp Fiction           8.3       8.207102        8428\n",
+              "65             The Dark Knight           8.2       8.136930       12002\n",
+              "1818          Schindler's List           8.3       8.126069        4329\n",
+              "3865                  Whiplash           8.3       8.123248        4254\n",
+              "809               Forrest Gump           8.2       8.105954        7927\n",
+              "2294             Spirited Away           8.3       8.105867        3840\n",
+              "2731    The Godfather: Part II           8.3       8.079586        3338"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c2eb2d12-582e-4f51-a10b-ffe6bdd0b2fd\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>title</th>\n",
+              "      <th>vote_average</th>\n",
+              "      <th>weighted_vote</th>\n",
+              "      <th>vote_count</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1881</th>\n",
+              "      <td>The Shawshank Redemption</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>8.396052</td>\n",
+              "      <td>8205</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3337</th>\n",
+              "      <td>The Godfather</td>\n",
+              "      <td>8.4</td>\n",
+              "      <td>8.263591</td>\n",
+              "      <td>5893</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>662</th>\n",
+              "      <td>Fight Club</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.216455</td>\n",
+              "      <td>9413</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3232</th>\n",
+              "      <td>Pulp Fiction</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.207102</td>\n",
+              "      <td>8428</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>65</th>\n",
+              "      <td>The Dark Knight</td>\n",
+              "      <td>8.2</td>\n",
+              "      <td>8.136930</td>\n",
+              "      <td>12002</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1818</th>\n",
+              "      <td>Schindler's List</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.126069</td>\n",
+              "      <td>4329</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3865</th>\n",
+              "      <td>Whiplash</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.123248</td>\n",
+              "      <td>4254</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>809</th>\n",
+              "      <td>Forrest Gump</td>\n",
+              "      <td>8.2</td>\n",
+              "      <td>8.105954</td>\n",
+              "      <td>7927</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2294</th>\n",
+              "      <td>Spirited Away</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.105867</td>\n",
+              "      <td>3840</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2731</th>\n",
+              "      <td>The Godfather: Part II</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.079586</td>\n",
+              "      <td>3338</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c2eb2d12-582e-4f51-a10b-ffe6bdd0b2fd')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c2eb2d12-582e-4f51-a10b-ffe6bdd0b2fd button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c2eb2d12-582e-4f51-a10b-ffe6bdd0b2fd');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-d9ee7182-3591-48e7-9d74-5d22454e75db\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d9ee7182-3591-48e7-9d74-5d22454e75db')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-d9ee7182-3591-48e7-9d74-5d22454e75db button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"movies_df[['title', 'vote_average', 'weighted_vote', 'vote_count']]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Spirited Away\",\n          \"The Godfather\",\n          \"Schindler's List\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08755950357709151,\n        \"min\": 8.2,\n        \"max\": 8.5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8.4,\n          8.2,\n          8.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weighted_vote\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09696608479450805,\n        \"min\": 8.07958629828635,\n        \"max\": 8.39605162693645,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          8.105867158639835,\n          8.263590802034972,\n          8.126068673669016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2866,\n        \"min\": 3338,\n        \"max\": 12002,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3840,\n          5893,\n          4329\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def find_sim_movie(df, sorted_ind, title_name, top_n = 10):\n",
+        "  title_movie = df[df['title'] == title_name]\n",
+        "  title_index = title_movie.index.values\n",
+        "\n",
+        "  similar_indexes = sorted_ind[title_index, :(top_n*2)]\n",
+        "  similar_indexes = similar_indexes.reshape(-1)\n",
+        "  similar_indexes = similar_indexes[similar_indexes != title_index]\n",
+        "\n",
+        "  return df.iloc[similar_indexes].sort_values('weighted_vote', ascending=False)[:top_n]\n",
+        "\n",
+        "similar_movies = find_sim_movie(movies_df, genre_sim_sorted_ind, 'The Godfather', 10)\n",
+        "similar_movies[['title', 'vote_average', 'weighted_vote']]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "id": "9saSifP2s7xV",
+        "outputId": "58c0b9e1-0ce1-45ea-9544-60fe28dbbc7e"
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                            title  vote_average  weighted_vote\n",
+              "1881     The Shawshank Redemption           8.5       8.396052\n",
+              "2731       The Godfather: Part II           8.3       8.079586\n",
+              "1847                   GoodFellas           8.2       7.976937\n",
+              "3866                  City of God           8.1       7.759693\n",
+              "1663  Once Upon a Time in America           8.2       7.657811\n",
+              "3887                Trainspotting           7.8       7.591009\n",
+              "883           Catch Me If You Can           7.7       7.557097\n",
+              "892                        Casino           7.8       7.423040\n",
+              "4041              This Is England           7.4       6.739664\n",
+              "1149              American Hustle           6.8       6.717525"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-01b41737-68b6-4e11-9e35-090596043661\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>title</th>\n",
+              "      <th>vote_average</th>\n",
+              "      <th>weighted_vote</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1881</th>\n",
+              "      <td>The Shawshank Redemption</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>8.396052</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2731</th>\n",
+              "      <td>The Godfather: Part II</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.079586</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1847</th>\n",
+              "      <td>GoodFellas</td>\n",
+              "      <td>8.2</td>\n",
+              "      <td>7.976937</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3866</th>\n",
+              "      <td>City of God</td>\n",
+              "      <td>8.1</td>\n",
+              "      <td>7.759693</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1663</th>\n",
+              "      <td>Once Upon a Time in America</td>\n",
+              "      <td>8.2</td>\n",
+              "      <td>7.657811</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3887</th>\n",
+              "      <td>Trainspotting</td>\n",
+              "      <td>7.8</td>\n",
+              "      <td>7.591009</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>883</th>\n",
+              "      <td>Catch Me If You Can</td>\n",
+              "      <td>7.7</td>\n",
+              "      <td>7.557097</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>892</th>\n",
+              "      <td>Casino</td>\n",
+              "      <td>7.8</td>\n",
+              "      <td>7.423040</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4041</th>\n",
+              "      <td>This Is England</td>\n",
+              "      <td>7.4</td>\n",
+              "      <td>6.739664</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1149</th>\n",
+              "      <td>American Hustle</td>\n",
+              "      <td>6.8</td>\n",
+              "      <td>6.717525</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-01b41737-68b6-4e11-9e35-090596043661')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-01b41737-68b6-4e11-9e35-090596043661 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-01b41737-68b6-4e11-9e35-090596043661');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-bfd98ede-b4dc-428e-8ecc-2d4d86056dd2\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bfd98ede-b4dc-428e-8ecc-2d4d86056dd2')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-bfd98ede-b4dc-428e-8ecc-2d4d86056dd2 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"similar_movies[['title', 'vote_average', 'weighted_vote']]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"This Is England\",\n          \"The Godfather: Part II\",\n          \"Trainspotting\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5006662228138289,\n        \"min\": 6.8,\n        \"max\": 8.5,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.3,\n          7.7,\n          8.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weighted_vote\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5360110086094774,\n        \"min\": 6.717525466229835,\n        \"max\": 8.39605162693645,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          6.739664363482589,\n          8.07958629828635,\n          7.591009490713154\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **06.아이템 기반 최근접 이웃 협업 필터링 실습**"
+      ],
+      "metadata": {
+        "id": "EPvqs026vyzE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "movies = pd.read_csv('/content/movies.csv')\n",
+        "ratings = pd.read_csv('/content/ratings.csv')\n",
+        "print(movies.shape)\n",
+        "print(ratings.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZRnCRT66v76Y",
+        "outputId": "1531ef33-a569-4772-9553-722b183c5be4"
+      },
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(9742, 3)\n",
+            "(100836, 4)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ratings = ratings[['userId', 'movieId', 'rating']]\n",
+        "#pivot_table()의 인자로 columns = 'movieId'와 같이 부여하면 movieId 칼럼의 모든 값이 새로운 칼럼 이름으로 변환\n",
+        "ratings_matrix = ratings.pivot_table('rating', index = 'userId', columns ='movieId')\n",
+        "ratings_matrix.head(3)"
+      ],
+      "metadata": {
+        "id": "2XjxD8d0w825",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "719af044-877b-4e88-ac1d-4b5b68932c8d"
+      },
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "movieId  1       2       3       4       5       6       7       8       \\\n",
+              "userId                                                                    \n",
+              "1           4.0     NaN     4.0     NaN     NaN     4.0     NaN     NaN   \n",
+              "2           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
+              "3           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
+              "\n",
+              "movieId  9       10      ...  193565  193567  193571  193573  193579  193581  \\\n",
+              "userId                   ...                                                   \n",
+              "1           NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
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+              "<p>3 rows × 9724 columns</p>\n",
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+              "    <div class=\"colab-df-buttons\">\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
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+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
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+              "      const buttonEl =\n",
+              "        document.querySelector('#df-00173ae7-a1c8-408a-82f4-1e227b8768ce button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-00173ae7-a1c8-408a-82f4-1e227b8768ce');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
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+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
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+              "      border-left-color: var(--fill-color);\n",
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+              "    30% {\n",
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+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
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+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
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+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-bd5065ff-035d-4ee5-a27d-b76f24b7cb87 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ratings_matrix"
+            }
+          },
+          "metadata": {},
+          "execution_count": 47
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* NaN 값이 많음: 사용자가 평점을 매기지 않은 영화가 칼럼으로 변환\n",
+        "* 최소평점이 0 => NaN은 모두 0으로 변환 NaN 값이 많음: 사용자가 평점을 매기지 않은 영화가 칼럼으로 변환\n",
+        "* 최소평점이 0 => NaN은 모두 0으로 변환"
+      ],
+      "metadata": {
+        "id": "TZUOwitLpKH4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rating_movies = pd.merge(ratings, movies, on='movieId')\n",
+        "\n",
+        "ratings_matrix = rating_movies.pivot_table('rating', index = 'userId', columns='title')\n",
+        "\n",
+        "ratings_matrix = ratings_matrix.fillna(0)\n",
+        "ratings_matrix.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 345
+        },
+        "id": "ZYUN-yaVpmhf",
+        "outputId": "d7e20cf8-9d0e-4aa1-cf44-348c943b1e73"
+      },
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
+              "userId                                                        \n",
+              "1              0.0                                      0.0   \n",
+              "2              0.0                                      0.0   \n",
+              "3              0.0                                      0.0   \n",
+              "\n",
+              "title   'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
+              "userId                                                \n",
+              "1                          0.0                  0.0   \n",
+              "2                          0.0                  0.0   \n",
+              "3                          0.0                  0.0   \n",
+              "\n",
+              "title   'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
+              "userId                                                               \n",
+              "1                             0.0                              0.0   \n",
+              "2                             0.0                              0.0   \n",
+              "3                             0.0                              0.0   \n",
+              "\n",
+              "title   'burbs, The (1989)  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
+              "userId                                                                          \n",
+              "1                      0.0                   0.0                          0.0   \n",
+              "2                      0.0                   0.0                          0.0   \n",
+              "3                      0.0                   0.0                          0.0   \n",
+              "\n",
+              "title   *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
+              "userId                                  ...                              \n",
+              "1                                  0.0  ...          0.0           0.0   \n",
+              "2                                  0.0  ...          0.0           0.0   \n",
+              "3                                  0.0  ...          0.0           0.0   \n",
+              "\n",
+              "title   [REC]² (2009)  [REC]³ 3 Génesis (2012)  \\\n",
+              "userId                                           \n",
+              "1                 0.0                      0.0   \n",
+              "2                 0.0                      0.0   \n",
+              "3                 0.0                      0.0   \n",
+              "\n",
+              "title   anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
+              "userId                                                           \n",
+              "1                                                          0.0   \n",
+              "2                                                          0.0   \n",
+              "3                                                          0.0   \n",
+              "\n",
+              "title   eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
+              "userId                                                                \n",
+              "1                   0.0         0.0                             0.0   \n",
+              "2                   0.0         0.0                             0.0   \n",
+              "3                   0.0         0.0                             0.0   \n",
+              "\n",
+              "title   ¡Three Amigos! (1986)  À nous la liberté (Freedom for Us) (1931)  \n",
+              "userId                                                                    \n",
+              "1                         4.0                                        0.0  \n",
+              "2                         0.0                                        0.0  \n",
+              "3                         0.0                                        0.0  \n",
+              "\n",
+              "[3 rows x 9719 columns]"
+            ],
+            "text/html": [
+              "\n",
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+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>title</th>\n",
+              "      <th>'71 (2014)</th>\n",
+              "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
+              "      <th>'Round Midnight (1986)</th>\n",
+              "      <th>'Salem's Lot (2004)</th>\n",
+              "      <th>'Til There Was You (1997)</th>\n",
+              "      <th>'Tis the Season for Love (2015)</th>\n",
+              "      <th>'burbs, The (1989)</th>\n",
+              "      <th>'night Mother (1986)</th>\n",
+              "      <th>(500) Days of Summer (2009)</th>\n",
+              "      <th>*batteries not included (1987)</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Zulu (2013)</th>\n",
+              "      <th>[REC] (2007)</th>\n",
+              "      <th>[REC]² (2009)</th>\n",
+              "      <th>[REC]³ 3 Génesis (2012)</th>\n",
+              "      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n",
+              "      <th>eXistenZ (1999)</th>\n",
+              "      <th>xXx (2002)</th>\n",
+              "      <th>xXx: State of the Union (2005)</th>\n",
+              "      <th>¡Three Amigos! (1986)</th>\n",
+              "      <th>À nous la liberté (Freedom for Us) (1931)</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>userId</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
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+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
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+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 9719 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
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+              "    .colab-df-container {\n",
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+              "    .colab-df-convert:hover {\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
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+              "        document.querySelector('#df-b0d6374d-8a1d-456c-8a83-738fcdcd1e52 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b0d6374d-8a1d-456c-8a83-738fcdcd1e52');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "\n",
+              "    <div id=\"df-0c6b2252-1898-4c44-8b20-53c0b471d4b6\">\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "      --disabled-bg-color: #3B4455;\n",
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+              "  .colab-df-quickchart {\n",
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+              "    height: 32px;\n",
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+              "    width: 32px;\n",
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+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
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+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
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+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "    30% {\n",
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+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
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+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
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+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
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+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
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+              "            console.error('Error during call to suggestCharts:', error);\n",
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+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ratings_matrix"
+            }
+          },
+          "metadata": {},
+          "execution_count": 48
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**영화 간 유사도 산출**\n",
+        "* cosine_similarity()는 행 간의 유사도 산출 but, 위 데이터에서는 영화가 아닌 userID간의 유사도가 산출됨"
+      ],
+      "metadata": {
+        "id": "-B-F7w8Fq0lk"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ratings_matrix_T = ratings_matrix.transpose()\n",
+        "ratings_matrix_T.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "id": "sI7bKBD2raXy",
+        "outputId": "48072932-af63-43fc-c201-1cb1f5805ee6"
+      },
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "userId                                   1    2    3    4    5    6    7    \\\n",
+              "title                                                                        \n",
+              "'71 (2014)                               0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
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+              "title                                                                       \n",
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+              "\n",
+              "[3 rows x 610 columns]"
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+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>'Round Midnight (1986)</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
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+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 610 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f72a1b8-9e88-4fdf-84ba-8c42d140b627')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4f72a1b8-9e88-4fdf-84ba-8c42d140b627 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4f72a1b8-9e88-4fdf-84ba-8c42d140b627');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-d2bce54f-6ccd-4c72-b8b2-e11fecc49365\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d2bce54f-6ccd-4c72-b8b2-e11fecc49365')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-d2bce54f-6ccd-4c72-b8b2-e11fecc49365 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ratings_matrix_T"
+            }
+          },
+          "metadata": {},
+          "execution_count": 49
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics.pairwise import cosine_similarity\n",
+        "\n",
+        "item_sim = cosine_similarity(ratings_matrix_T, ratings_matrix_T)\n",
+        "\n",
+        "item_sim_df = pd.DataFrame(data = item_sim, index = ratings_matrix.columns,\n",
+        "                           columns = ratings_matrix.columns)\n",
+        "print(item_sim_df.shape)\n",
+        "item_sim_df.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 485
+        },
+        "id": "LLg_HOABvdQQ",
+        "outputId": "2815ccfa-189c-45c6-c180-76510ec6f607"
+      },
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(9719, 9719)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title                                    '71 (2014)  \\\n",
+              "title                                                 \n",
+              "'71 (2014)                                      1.0   \n",
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+              "'Round Midnight (1986)                          0.0   \n",
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+              "title                                    'Hellboy': The Seeds of Creation (2004)  \\\n",
+              "title                                                                              \n",
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+              "title                                    'Round Midnight (1986)  \\\n",
+              "title                                                             \n",
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+              "'Round Midnight (1986)                                 1.000000   \n",
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+              "title                                    'Salem's Lot (2004)  \\\n",
+              "title                                                          \n",
+              "'71 (2014)                                               0.0   \n",
+              "'Hellboy': The Seeds of Creation (2004)                  0.0   \n",
+              "'Round Midnight (1986)                                   0.0   \n",
+              "\n",
+              "title                                    'Til There Was You (1997)  \\\n",
+              "title                                                                \n",
+              "'71 (2014)                                                     0.0   \n",
+              "'Hellboy': The Seeds of Creation (2004)                        0.0   \n",
+              "'Round Midnight (1986)                                         0.0   \n",
+              "\n",
+              "title                                    'Tis the Season for Love (2015)  \\\n",
+              "title                                                                      \n",
+              "'71 (2014)                                                           0.0   \n",
+              "'Hellboy': The Seeds of Creation (2004)                              0.0   \n",
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+              "\n",
+              "title                                    'burbs, The (1989)  \\\n",
+              "title                                                         \n",
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+              "'Round Midnight (1986)                             0.176777   \n",
+              "\n",
+              "title                                    'night Mother (1986)  \\\n",
+              "title                                                           \n",
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+              "'Round Midnight (1986)                                    0.0   \n",
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+              "title                                    (500) Days of Summer (2009)  \\\n",
+              "title                                                                  \n",
+              "'71 (2014)                                                  0.141653   \n",
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+              "title                                                                    ...   \n",
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+              "'Round Midnight (1986)                                              0.0  ...   \n",
+              "\n",
+              "title                                    Zulu (2013)  [REC] (2007)  \\\n",
+              "title                                                                \n",
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+              "'Round Midnight (1986)                           0.0      0.000000   \n",
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+              "title                                    [REC]² (2009)  \\\n",
+              "title                                                    \n",
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+              "'Hellboy': The Seeds of Creation (2004)       0.000000   \n",
+              "'Round Midnight (1986)                        0.000000   \n",
+              "\n",
+              "title                                    [REC]³ 3 Génesis (2012)  \\\n",
+              "title                                                              \n",
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+              "'Round Midnight (1986)                                  0.000000   \n",
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+              "title                                    anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
+              "title                                                                                             \n",
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+              "title                                                                  \n",
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+              "title                                    xXx: State of the Union (2005)  \\\n",
+              "title                                                                     \n",
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+              "title                                    ¡Three Amigos! (1986)  \\\n",
+              "title                                                            \n",
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+              "title                                                                               \n",
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+              "'Hellboy': The Seeds of Creation (2004)                                        0.0  \n",
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+            ],
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+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>'Round Midnight (1986)</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.707107</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.176777</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 9719 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6a671121-391a-4651-9059-f8b08186ccbf')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-6a671121-391a-4651-9059-f8b08186ccbf button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-6a671121-391a-4651-9059-f8b08186ccbf');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-740d5ed8-927a-4168-8e5b-df2dbbb47aec\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-740d5ed8-927a-4168-8e5b-df2dbbb47aec')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-740d5ed8-927a-4168-8e5b-df2dbbb47aec button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "item_sim_df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 50
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "item_sim_df[\"Godfather, The (1972)\"].sort_values(ascending=False)[:6]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 303
+        },
+        "id": "U2H_-HrYwSVw",
+        "outputId": "84922a7f-cf0b-4d6d-89cc-451c035638e4"
+      },
+      "execution_count": 51,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title\n",
+              "Godfather, The (1972)                        1.000000\n",
+              "Godfather: Part II, The (1974)               0.821773\n",
+              "Goodfellas (1990)                            0.664841\n",
+              "One Flew Over the Cuckoo's Nest (1975)       0.620536\n",
+              "Star Wars: Episode IV - A New Hope (1977)    0.595317\n",
+              "Fargo (1996)                                 0.588614\n",
+              "Name: Godfather, The (1972), dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Godfather, The (1972)</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>title</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Godfather, The (1972)</th>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Godfather: Part II, The (1974)</th>\n",
+              "      <td>0.821773</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Goodfellas (1990)</th>\n",
+              "      <td>0.664841</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>One Flew Over the Cuckoo's Nest (1975)</th>\n",
+              "      <td>0.620536</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Star Wars: Episode IV - A New Hope (1977)</th>\n",
+              "      <td>0.595317</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Fargo (1996)</th>\n",
+              "      <td>0.588614</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 51
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "item_sim_df[\"Inception (2010)\"].sort_values(ascending=False)[1:6]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 272
+        },
+        "id": "6WdgQhjiwmjR",
+        "outputId": "ebcca204-3fbb-4242-ccdc-8e8bf25201e6"
+      },
+      "execution_count": 52,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title\n",
+              "Dark Knight, The (2008)          0.727263\n",
+              "Inglourious Basterds (2009)      0.646103\n",
+              "Shutter Island (2010)            0.617736\n",
+              "Dark Knight Rises, The (2012)    0.617504\n",
+              "Fight Club (1999)                0.615417\n",
+              "Name: Inception (2010), dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Inception (2010)</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>title</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Dark Knight, The (2008)</th>\n",
+              "      <td>0.727263</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Inglourious Basterds (2009)</th>\n",
+              "      <td>0.646103</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Shutter Island (2010)</th>\n",
+              "      <td>0.617736</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Dark Knight Rises, The (2012)</th>\n",
+              "      <td>0.617504</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Fight Club (1999)</th>\n",
+              "      <td>0.615417</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 52
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**아이템 기반 최근접 이웃 협업 필터링으로 개인화된 영화 추천**\n",
+        "![화면 캡처 2025-12-24 005955.png](data:image/png;base64,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)"
+      ],
+      "metadata": {
+        "id": "7kVnf14sw3_v"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "![화면 캡처 2025-12-24 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)"
+      ],
+      "metadata": {
+        "id": "5fiwPSG48Nr7"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* N값은 아이템의 최근접 이웃 범위 계수를 의미-> 특정 아이템과 유사도가 가장 높은 Top-N개의 다른 아이템을 추출하는데 사용"
+      ],
+      "metadata": {
+        "id": "Pb7HZydc8e-i"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def predict_rating(ratings_arr, item_sim_arr):\n",
+        "  ratings_pred = ratings_arr.dot(item_sim_arr)/np.array([np.abs(item_sim_arr).sum(axis = 1)])\n",
+        "  return ratings_pred"
+      ],
+      "metadata": {
+        "id": "Gm7XQcyY9I86"
+      },
+      "execution_count": 53,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#예측 평점\n",
+        "ratings_pred = predict_rating(ratings_matrix.values, item_sim_df.values)\n",
+        "ratings_pred_matrix = pd.DataFrame(data=ratings_pred, index = ratings_matrix.index,\n",
+        "                                   columns = ratings_matrix.columns)\n",
+        "ratings_pred_matrix.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 345
+        },
+        "id": "aaHeQJkeqzS0",
+        "outputId": "b3de8ff7-4235-48c3-eead-c51fc1a20b21"
+      },
+      "execution_count": 54,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
+              "userId                                                        \n",
+              "1         0.070345                                 0.577855   \n",
+              "2         0.018260                                 0.042744   \n",
+              "3         0.011884                                 0.030279   \n",
+              "\n",
+              "title   'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
+              "userId                                                \n",
+              "1                     0.321696             0.227055   \n",
+              "2                     0.018861             0.000000   \n",
+              "3                     0.064437             0.003762   \n",
+              "\n",
+              "title   'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
+              "userId                                                               \n",
+              "1                        0.206958                         0.194615   \n",
+              "2                        0.000000                         0.035995   \n",
+              "3                        0.003749                         0.002722   \n",
+              "\n",
+              "title   'burbs, The (1989)  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
+              "userId                                                                          \n",
+              "1                 0.249883              0.102542                     0.157084   \n",
+              "2                 0.013413              0.002314                     0.032213   \n",
+              "3                 0.014625              0.002085                     0.005666   \n",
+              "\n",
+              "title   *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
+              "userId                                  ...                              \n",
+              "1                             0.178197  ...     0.113608      0.181738   \n",
+              "2                             0.014863  ...     0.015640      0.020855   \n",
+              "3                             0.006272  ...     0.006923      0.011665   \n",
+              "\n",
+              "title   [REC]² (2009)  [REC]³ 3 Génesis (2012)  \\\n",
+              "userId                                           \n",
+              "1            0.133962                 0.128574   \n",
+              "2            0.020119                 0.015745   \n",
+              "3            0.011800                 0.012225   \n",
+              "\n",
+              "title   anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
+              "userId                                                           \n",
+              "1                                                     0.006179   \n",
+              "2                                                     0.049983   \n",
+              "3                                                     0.000000   \n",
+              "\n",
+              "title   eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
+              "userId                                                                \n",
+              "1              0.212070    0.192921                        0.136024   \n",
+              "2              0.014876    0.021616                        0.024528   \n",
+              "3              0.008194    0.007017                        0.009229   \n",
+              "\n",
+              "title   ¡Three Amigos! (1986)  À nous la liberté (Freedom for Us) (1931)  \n",
+              "userId                                                                    \n",
+              "1                    0.292955                                   0.720347  \n",
+              "2                    0.017563                                   0.000000  \n",
+              "3                    0.010420                                   0.084501  \n",
+              "\n",
+              "[3 rows x 9719 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-1fa5d483-ea2f-4d13-915c-8088ade937a0\" class=\"colab-df-container\">\n",
+              "    <div>\n",
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+              "        vertical-align: middle;\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>title</th>\n",
+              "      <th>'71 (2014)</th>\n",
+              "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
+              "      <th>'Round Midnight (1986)</th>\n",
+              "      <th>'Salem's Lot (2004)</th>\n",
+              "      <th>'Til There Was You (1997)</th>\n",
+              "      <th>'Tis the Season for Love (2015)</th>\n",
+              "      <th>'burbs, The (1989)</th>\n",
+              "      <th>'night Mother (1986)</th>\n",
+              "      <th>(500) Days of Summer (2009)</th>\n",
+              "      <th>*batteries not included (1987)</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Zulu (2013)</th>\n",
+              "      <th>[REC] (2007)</th>\n",
+              "      <th>[REC]² (2009)</th>\n",
+              "      <th>[REC]³ 3 Génesis (2012)</th>\n",
+              "      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n",
+              "      <th>eXistenZ (1999)</th>\n",
+              "      <th>xXx (2002)</th>\n",
+              "      <th>xXx: State of the Union (2005)</th>\n",
+              "      <th>¡Three Amigos! (1986)</th>\n",
+              "      <th>À nous la liberté (Freedom for Us) (1931)</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>userId</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.070345</td>\n",
+              "      <td>0.577855</td>\n",
+              "      <td>0.321696</td>\n",
+              "      <td>0.227055</td>\n",
+              "      <td>0.206958</td>\n",
+              "      <td>0.194615</td>\n",
+              "      <td>0.249883</td>\n",
+              "      <td>0.102542</td>\n",
+              "      <td>0.157084</td>\n",
+              "      <td>0.178197</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.113608</td>\n",
+              "      <td>0.181738</td>\n",
+              "      <td>0.133962</td>\n",
+              "      <td>0.128574</td>\n",
+              "      <td>0.006179</td>\n",
+              "      <td>0.212070</td>\n",
+              "      <td>0.192921</td>\n",
+              "      <td>0.136024</td>\n",
+              "      <td>0.292955</td>\n",
+              "      <td>0.720347</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.018260</td>\n",
+              "      <td>0.042744</td>\n",
+              "      <td>0.018861</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.035995</td>\n",
+              "      <td>0.013413</td>\n",
+              "      <td>0.002314</td>\n",
+              "      <td>0.032213</td>\n",
+              "      <td>0.014863</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.015640</td>\n",
+              "      <td>0.020855</td>\n",
+              "      <td>0.020119</td>\n",
+              "      <td>0.015745</td>\n",
+              "      <td>0.049983</td>\n",
+              "      <td>0.014876</td>\n",
+              "      <td>0.021616</td>\n",
+              "      <td>0.024528</td>\n",
+              "      <td>0.017563</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.011884</td>\n",
+              "      <td>0.030279</td>\n",
+              "      <td>0.064437</td>\n",
+              "      <td>0.003762</td>\n",
+              "      <td>0.003749</td>\n",
+              "      <td>0.002722</td>\n",
+              "      <td>0.014625</td>\n",
+              "      <td>0.002085</td>\n",
+              "      <td>0.005666</td>\n",
+              "      <td>0.006272</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.006923</td>\n",
+              "      <td>0.011665</td>\n",
+              "      <td>0.011800</td>\n",
+              "      <td>0.012225</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.008194</td>\n",
+              "      <td>0.007017</td>\n",
+              "      <td>0.009229</td>\n",
+              "      <td>0.010420</td>\n",
+              "      <td>0.084501</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 9719 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1fa5d483-ea2f-4d13-915c-8088ade937a0')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-1fa5d483-ea2f-4d13-915c-8088ade937a0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-1fa5d483-ea2f-4d13-915c-8088ade937a0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-ffde91bf-69e5-45c4-8857-03d33f11a312\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ffde91bf-69e5-45c4-8857-03d33f11a312')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-ffde91bf-69e5-45c4-8857-03d33f11a312 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ratings_pred_matrix"
+            }
+          },
+          "metadata": {},
+          "execution_count": 54
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#실제 평점과의 차이\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "def get_mse(pred, actual):\n",
+        "  pred = pred[actual.nonzero()].flatten()\n",
+        "  actual = actual[actual.nonzero()].flatten()\n",
+        "  return mean_squared_error(pred, actual)\n",
+        "\n",
+        "print('아이템 기반 모든 최근접 이웃 MSE:', get_mse(ratings_pred, ratings_matrix.values))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "svZOAsty--90",
+        "outputId": "519c8ad1-ceff-4dc8-8825-fa207d71a04d"
+      },
+      "execution_count": 55,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "아이템 기반 모든 최근접 이웃 MSE: 9.895354759094706\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def predict_rating_topsim(ratings_arr, item_sim_arr, n = 20):\n",
+        "  pred = np.zeros(ratings_arr.shape)\n",
+        "\n",
+        "  for col in range(ratings_arr.shape[1]):\n",
+        "    top_n_items = [np.argsort(item_sim_arr[:, col])[:-n-1:-1]]\n",
+        "    for row in range(ratings_arr.shape[0]):\n",
+        "      pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row,:][top_n_items].T)\n",
+        "      pred[row, col] /= np.sum(np.abs(item_sim_arr[col, :][top_n_items]))\n",
+        "\n",
+        "  return pred"
+      ],
+      "metadata": {
+        "id": "8oTvBsktAPNK"
+      },
+      "execution_count": 56,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ratings_pred = predict_rating_topsim(ratings_matrix.values, item_sim_df.values, n = 20)\n",
+        "print('아이템 기반 최근접 Top-20 이웃 MSE: ', get_mse(ratings_pred, ratings_matrix.values))\n",
+        "\n",
+        "ratings_pred_matrix = pd.DataFrame(data = ratings_pred, index = ratings_matrix.index, columns= ratings_matrix.columns)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zb1_ffRyBFeq",
+        "outputId": "68bd82c5-fa6c-40c9-d822-7f1ba1e665b7"
+      },
+      "execution_count": 57,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "아이템 기반 최근접 Top-20 이웃 MSE:  3.694409449382562\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "user_rating_id = ratings_matrix.loc[9, :]\n",
+        "user_rating_id[user_rating_id >0].sort_values(ascending=False)[:10]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 429
+        },
+        "id": "R5Bqv4mJBhiD",
+        "outputId": "8db5c4d2-feae-48ee-938f-9f455b3cace6"
+      },
+      "execution_count": 58,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title\n",
+              "Adaptation (2002)                                                                 5.0\n",
+              "Austin Powers in Goldmember (2002)                                                5.0\n",
+              "Back to the Future (1985)                                                         5.0\n",
+              "Citizen Kane (1941)                                                               5.0\n",
+              "Lord of the Rings: The Fellowship of the Ring, The (2001)                         5.0\n",
+              "Lord of the Rings: The Two Towers, The (2002)                                     5.0\n",
+              "Producers, The (1968)                                                             5.0\n",
+              "Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)    5.0\n",
+              "Elling (2001)                                                                     4.0\n",
+              "King of Comedy, The (1983)                                                        4.0\n",
+              "Name: 9, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>9</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>title</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Adaptation (2002)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Austin Powers in Goldmember (2002)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Back to the Future (1985)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Citizen Kane (1941)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Lord of the Rings: The Fellowship of the Ring, The (2001)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Lord of the Rings: The Two Towers, The (2002)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Producers, The (1968)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Elling (2001)</th>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>King of Comedy, The (1983)</th>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 58
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_unseen_movies(ratings_matrix, userId):\n",
+        "  user_rating = ratings_matrix.loc[userId, :]\n",
+        "  already_seen = user_rating[user_rating >0].index.tolist()\n",
+        "  movies_list = ratings_matrix.columns.tolist()\n",
+        "  unseen_list = [movie for movie in movies_list if movie not in already_seen]\n",
+        "  return unseen_list"
+      ],
+      "metadata": {
+        "id": "Ad--lUP_BvfG"
+      },
+      "execution_count": 59,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def recomm_movie_by_userId(pred_df, userId, unseen_list, top_n=10):\n",
+        "  recomm_movies = pred_df.loc[userId, unseen_list].sort_values(ascending = False)[:top_n]\n",
+        "  return recomm_movies\n",
+        "\n",
+        "unseen_list = get_unseen_movies(ratings_matrix, 9)\n",
+        "\n",
+        "recomm_movies = recomm_movie_by_userId(ratings_pred_matrix, 9, unseen_list, top_n = 10)\n",
+        "\n",
+        "recomm_movies = pd.DataFrame(data = recomm_movies.values, index = recomm_movies.index,\n",
+        "                             columns=['pred_score'])\n",
+        "recomm_movies"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 394
+        },
+        "id": "HynFIaJqCYX5",
+        "outputId": "cb11513f-726e-4dfd-d706-915beda9a938"
+      },
+      "execution_count": 60,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                                                                pred_score\n",
+              "title                                                                                                     \n",
+              "Shrek (2001)                                                                                      0.866202\n",
+              "Spider-Man (2002)                                                                                 0.857854\n",
+              "Last Samurai, The (2003)                                                                          0.817473\n",
+              "Indiana Jones and the Temple of Doom (1984)                                                       0.816626\n",
+              "Matrix Reloaded, The (2003)                                                                       0.800990\n",
+              "Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001)    0.765159\n",
+              "Gladiator (2000)                                                                                  0.740956\n",
+              "Matrix, The (1999)                                                                                0.732693\n",
+              "Pirates of the Caribbean: The Curse of the Black Pearl (2003)                                     0.689591\n",
+              "Lord of the Rings: The Return of the King, The (2003)                                             0.676711"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ae94d2bc-02cd-46c0-bad4-fcf6b62c1d9e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>pred_score</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>title</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Shrek (2001)</th>\n",
+              "      <td>0.866202</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Spider-Man (2002)</th>\n",
+              "      <td>0.857854</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Last Samurai, The (2003)</th>\n",
+              "      <td>0.817473</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Indiana Jones and the Temple of Doom (1984)</th>\n",
+              "      <td>0.816626</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Matrix Reloaded, The (2003)</th>\n",
+              "      <td>0.800990</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001)</th>\n",
+              "      <td>0.765159</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Gladiator (2000)</th>\n",
+              "      <td>0.740956</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Matrix, The (1999)</th>\n",
+              "      <td>0.732693</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Pirates of the Caribbean: The Curse of the Black Pearl (2003)</th>\n",
+              "      <td>0.689591</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Lord of the Rings: The Return of the King, The (2003)</th>\n",
+              "      <td>0.676711</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae94d2bc-02cd-46c0-bad4-fcf6b62c1d9e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-ae94d2bc-02cd-46c0-bad4-fcf6b62c1d9e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ae94d2bc-02cd-46c0-bad4-fcf6b62c1d9e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-c0a2a470-6018-44db-97d3-fdcbcd6f2ac7\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c0a2a470-6018-44db-97d3-fdcbcd6f2ac7')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-c0a2a470-6018-44db-97d3-fdcbcd6f2ac7 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_75d4f7cb-98d7-400e-9d83-b9035180f57a\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('recomm_movies')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_75d4f7cb-98d7-400e-9d83-b9035180f57a button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('recomm_movies');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "recomm_movies",
+              "summary": "{\n  \"name\": \"recomm_movies\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Pirates of the Caribbean: The Curse of the Black Pearl (2003)\",\n          \"Spider-Man (2002)\",\n          \"Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pred_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06614432811511851,\n        \"min\": 0.6767108283499336,\n        \"max\": 0.8662018746933645,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.6895905595608812,\n          0.8578535950426878,\n          0.7651586070058114\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 60
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#**07.행렬 분해를 이용한 잠재 요인 협업 필터링 실습**\n"
+      ],
+      "metadata": {
+        "id": "UL4MPaLtDKba"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "def get_rmse(R, P, Q, non_zeros):\n",
+        "  error = 0\n",
+        "  full_pred_matrix = np.dot(P, Q.T)\n",
+        "\n",
+        "  x_non_zero_ind = [non_zero[0] for non_zero in non_zeros]\n",
+        "  y_non_zero_ind = [non_zero[1] for non_zero in non_zeros]\n",
+        "  R_non_zeros = R[x_non_zero_ind, y_non_zero_ind]\n",
+        "  full_pred_matrix_non_zeros = full_pred_matrix[x_non_zero_ind, y_non_zero_ind]\n",
+        "  mse = mean_squared_error(R_non_zeros, full_pred_matrix_non_zeros)\n",
+        "  rmse = np.sqrt(mse)"
+      ],
+      "metadata": {
+        "id": "eXr27imETiQN"
+      },
+      "execution_count": 64,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def matrix_factorization(R, K, steps = 200, learning_rate= 0.01, r_lambda = 0.01):\n",
+        "  num_users, num_items = R.shape\n",
+        "  np.random.seed(1)\n",
+        "\n",
+        "  P = np.random.normal(scale = 1./K, size = (num_users, K))\n",
+        "  Q = np.random.normal(scale = 1./K, size = (num_items, K))\n",
+        "\n",
+        "  non_zeros = [(i, j ,R[i,j]) for i in range(num_users) for j in range(num_items) if R[i, j]>0]\n",
+        "  for step in range(steps):\n",
+        "    for i, j, r in non_zeros:\n",
+        "      eij = r-np.dot(P[i, :], Q[j, :].T)\n",
+        "      P[i, :] = P[i,:] + learning_rate*(eij*Q[j, :]-r_lambda*P[i, :])\n",
+        "      Q[j, :] = Q[j,:] + learning_rate*(eij*P[i, :]-r_lambda*Q[j, :])\n",
+        "\n",
+        "    rmse = get_rmse(R, P, Q, non_zeros)\n",
+        "    if(step%10) == 0:\n",
+        "      print('### iteration step:', step, \"rmse:\", rmse)\n",
+        "  return P,Q"
+      ],
+      "metadata": {
+        "id": "SiZFW1s4IJY9"
+      },
+      "execution_count": 65,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "movies = pd.read_csv('/content/movies.csv')\n",
+        "ratings = pd.read_csv('/content/ratings.csv')\n",
+        "ratings = ratings[['userId', 'movieId', 'rating']]\n",
+        "ratings_matrix = ratings.pivot_table('rating', index = 'userId', columns = 'movieId')\n",
+        "\n",
+        "rating_movies = pd.merge(ratings, movies, on = 'movieId')\n",
+        "ratings_matrix = rating_movies.pivot_table('rating', index = 'userId', columns = 'title')"
+      ],
+      "metadata": {
+        "id": "MwdnEOAzJgkw"
+      },
+      "execution_count": 66,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "P, Q = matrix_factorization(ratings_matrix.values, K=50, steps=200, learning_rate=0.01, r_lambda = 0.01)\n",
+        "pred_matrix = np.dot(P, Q.T)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3emxwCD4KIV8",
+        "outputId": "1779c8f6-317e-4183-a6cb-549a076bb808"
+      },
+      "execution_count": 67,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "### iteration step: 0 rmse: None\n",
+            "### iteration step: 10 rmse: None\n",
+            "### iteration step: 20 rmse: None\n",
+            "### iteration step: 30 rmse: None\n",
+            "### iteration step: 40 rmse: None\n",
+            "### iteration step: 50 rmse: None\n",
+            "### iteration step: 60 rmse: None\n",
+            "### iteration step: 70 rmse: None\n",
+            "### iteration step: 80 rmse: None\n",
+            "### iteration step: 90 rmse: None\n",
+            "### iteration step: 100 rmse: None\n",
+            "### iteration step: 110 rmse: None\n",
+            "### iteration step: 120 rmse: None\n",
+            "### iteration step: 130 rmse: None\n",
+            "### iteration step: 140 rmse: None\n",
+            "### iteration step: 150 rmse: None\n",
+            "### iteration step: 160 rmse: None\n",
+            "### iteration step: 170 rmse: None\n",
+            "### iteration step: 180 rmse: None\n",
+            "### iteration step: 190 rmse: None\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ratings_pred_matrix = pd.DataFrame(data=pred_matrix, index= ratings_matrix.index, columns = ratings_matrix.columns)\n",
+        "ratings_pred_matrix.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 345
+        },
+        "id": "tRE-gmHYKQYx",
+        "outputId": "f60d1125-3123-4d4e-801d-f87c6178fb85"
+      },
+      "execution_count": 68,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
+              "userId                                                        \n",
+              "1         3.055084                                 4.092018   \n",
+              "2         3.170119                                 3.657992   \n",
+              "3         2.307073                                 1.658853   \n",
+              "\n",
+              "title   'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
+              "userId                                                \n",
+              "1                     3.564130             4.502167   \n",
+              "2                     3.308707             4.166521   \n",
+              "3                     1.443538             2.208859   \n",
+              "\n",
+              "title   'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
+              "userId                                                               \n",
+              "1                        3.981215                         1.271694   \n",
+              "2                        4.311890                         1.275469   \n",
+              "3                        2.229486                         0.780760   \n",
+              "\n",
+              "title   'burbs, The (1989)  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
+              "userId                                                                          \n",
+              "1                 3.603274              2.333266                     5.091749   \n",
+              "2                 4.237972              1.900366                     3.392859   \n",
+              "3                 1.997043              0.924908                     2.970700   \n",
+              "\n",
+              "title   *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
+              "userId                                  ...                              \n",
+              "1                             3.972454  ...     1.402608      4.208382   \n",
+              "2                             3.647421  ...     0.973811      3.528264   \n",
+              "3                             2.551446  ...     0.520354      1.709494   \n",
+              "\n",
+              "title   [REC]² (2009)  [REC]³ 3 Génesis (2012)  \\\n",
+              "userId                                           \n",
+              "1            3.705957                 2.720514   \n",
+              "2            3.361532                 2.672535   \n",
+              "3            2.281596                 1.782833   \n",
+              "\n",
+              "title   anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
+              "userId                                                           \n",
+              "1                                                     2.787331   \n",
+              "2                                                     2.404456   \n",
+              "3                                                     1.635173   \n",
+              "\n",
+              "title   eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
+              "userId                                                                \n",
+              "1              3.475076    3.253458                        2.161087   \n",
+              "2              4.232789    2.911602                        1.634576   \n",
+              "3              1.323276    2.887580                        1.042618   \n",
+              "\n",
+              "title   ¡Three Amigos! (1986)  À nous la liberté (Freedom for Us) (1931)  \n",
+              "userId                                                                    \n",
+              "1                    4.010495                                   0.859474  \n",
+              "2                    4.135735                                   0.725684  \n",
+              "3                    2.293890                                   0.396941  \n",
+              "\n",
+              "[3 rows x 9719 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-660fbe46-f977-448f-b56c-f1ca03b74775\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>title</th>\n",
+              "      <th>'71 (2014)</th>\n",
+              "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
+              "      <th>'Round Midnight (1986)</th>\n",
+              "      <th>'Salem's Lot (2004)</th>\n",
+              "      <th>'Til There Was You (1997)</th>\n",
+              "      <th>'Tis the Season for Love (2015)</th>\n",
+              "      <th>'burbs, The (1989)</th>\n",
+              "      <th>'night Mother (1986)</th>\n",
+              "      <th>(500) Days of Summer (2009)</th>\n",
+              "      <th>*batteries not included (1987)</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Zulu (2013)</th>\n",
+              "      <th>[REC] (2007)</th>\n",
+              "      <th>[REC]² (2009)</th>\n",
+              "      <th>[REC]³ 3 Génesis (2012)</th>\n",
+              "      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n",
+              "      <th>eXistenZ (1999)</th>\n",
+              "      <th>xXx (2002)</th>\n",
+              "      <th>xXx: State of the Union (2005)</th>\n",
+              "      <th>¡Three Amigos! (1986)</th>\n",
+              "      <th>À nous la liberté (Freedom for Us) (1931)</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>userId</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>3.055084</td>\n",
+              "      <td>4.092018</td>\n",
+              "      <td>3.564130</td>\n",
+              "      <td>4.502167</td>\n",
+              "      <td>3.981215</td>\n",
+              "      <td>1.271694</td>\n",
+              "      <td>3.603274</td>\n",
+              "      <td>2.333266</td>\n",
+              "      <td>5.091749</td>\n",
+              "      <td>3.972454</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.402608</td>\n",
+              "      <td>4.208382</td>\n",
+              "      <td>3.705957</td>\n",
+              "      <td>2.720514</td>\n",
+              "      <td>2.787331</td>\n",
+              "      <td>3.475076</td>\n",
+              "      <td>3.253458</td>\n",
+              "      <td>2.161087</td>\n",
+              "      <td>4.010495</td>\n",
+              "      <td>0.859474</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3.170119</td>\n",
+              "      <td>3.657992</td>\n",
+              "      <td>3.308707</td>\n",
+              "      <td>4.166521</td>\n",
+              "      <td>4.311890</td>\n",
+              "      <td>1.275469</td>\n",
+              "      <td>4.237972</td>\n",
+              "      <td>1.900366</td>\n",
+              "      <td>3.392859</td>\n",
+              "      <td>3.647421</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.973811</td>\n",
+              "      <td>3.528264</td>\n",
+              "      <td>3.361532</td>\n",
+              "      <td>2.672535</td>\n",
+              "      <td>2.404456</td>\n",
+              "      <td>4.232789</td>\n",
+              "      <td>2.911602</td>\n",
+              "      <td>1.634576</td>\n",
+              "      <td>4.135735</td>\n",
+              "      <td>0.725684</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2.307073</td>\n",
+              "      <td>1.658853</td>\n",
+              "      <td>1.443538</td>\n",
+              "      <td>2.208859</td>\n",
+              "      <td>2.229486</td>\n",
+              "      <td>0.780760</td>\n",
+              "      <td>1.997043</td>\n",
+              "      <td>0.924908</td>\n",
+              "      <td>2.970700</td>\n",
+              "      <td>2.551446</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.520354</td>\n",
+              "      <td>1.709494</td>\n",
+              "      <td>2.281596</td>\n",
+              "      <td>1.782833</td>\n",
+              "      <td>1.635173</td>\n",
+              "      <td>1.323276</td>\n",
+              "      <td>2.887580</td>\n",
+              "      <td>1.042618</td>\n",
+              "      <td>2.293890</td>\n",
+              "      <td>0.396941</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 9719 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
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+              "\n",
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+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
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+              "      const buttonEl =\n",
+              "        document.querySelector('#df-660fbe46-f977-448f-b56c-f1ca03b74775 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-660fbe46-f977-448f-b56c-f1ca03b74775');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
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+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-5401c409-b6d0-43ec-b22d-1014b7d33b43 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ratings_pred_matrix"
+            }
+          },
+          "metadata": {},
+          "execution_count": 68
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 사용자가 관람하지 않은 영화명 추출\n",
+        "unseen_list = get_unseen_movies(ratings_matrix, 9)\n",
+        "# 잠재 요인 협업 필터링으로 영화 추천\n",
+        "recomm_movies = recomm_movie_by_userId(ratings_pred_matrix, 9, unseen_list, top_n=10)\n",
+        "# 평점 데이터를 DataFrame으로 생성.\n",
+        "recomm_movies = pd.DataFrame(data=recomm_movies.values, index=recomm_movies.index, columns=['pred_score'])\n",
+        "recomm_movies"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 394
+        },
+        "id": "D2_UAkzhKei7",
+        "outputId": "3acbdbd3-6a27-4966-c5c5-8f561c97f53a"
+      },
+      "execution_count": 69,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                       pred_score\n",
+              "title                                                            \n",
+              "Rear Window (1954)                                       5.704612\n",
+              "South Park: Bigger, Longer and Uncut (1999)              5.451100\n",
+              "Rounders (1998)                                          5.298393\n",
+              "Blade Runner (1982)                                      5.244951\n",
+              "Roger & Me (1989)                                        5.191962\n",
+              "Gattaca (1997)                                           5.183179\n",
+              "Ben-Hur (1959)                                           5.130463\n",
+              "Rosencrantz and Guildenstern Are Dead (1990)             5.087375\n",
+              "Big Lebowski, The (1998)                                 5.038690\n",
+              "Star Wars: Episode V - The Empire Strikes Back (1980)    4.989601"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-eb597e58-9674-41b7-bbc4-c8d367928d15\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>pred_score</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>title</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Rear Window (1954)</th>\n",
+              "      <td>5.704612</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>South Park: Bigger, Longer and Uncut (1999)</th>\n",
+              "      <td>5.451100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rounders (1998)</th>\n",
+              "      <td>5.298393</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Blade Runner (1982)</th>\n",
+              "      <td>5.244951</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Roger &amp; Me (1989)</th>\n",
+              "      <td>5.191962</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Gattaca (1997)</th>\n",
+              "      <td>5.183179</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Ben-Hur (1959)</th>\n",
+              "      <td>5.130463</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rosencrantz and Guildenstern Are Dead (1990)</th>\n",
+              "      <td>5.087375</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Big Lebowski, The (1998)</th>\n",
+              "      <td>5.038690</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Star Wars: Episode V - The Empire Strikes Back (1980)</th>\n",
+              "      <td>4.989601</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb597e58-9674-41b7-bbc4-c8d367928d15')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-eb597e58-9674-41b7-bbc4-c8d367928d15 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-eb597e58-9674-41b7-bbc4-c8d367928d15');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-f9dd337b-a112-49b6-9baa-de6c1948ea0c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f9dd337b-a112-49b6-9baa-de6c1948ea0c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-f9dd337b-a112-49b6-9baa-de6c1948ea0c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_7df08a8e-e235-4ace-bdb6-b9fafaae8993\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('recomm_movies')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_7df08a8e-e235-4ace-bdb6-b9fafaae8993 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('recomm_movies');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "recomm_movies",
+              "summary": "{\n  \"name\": \"recomm_movies\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Big Lebowski, The (1998)\",\n          \"South Park: Bigger, Longer and Uncut (1999)\",\n          \"Gattaca (1997)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pred_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21272885538651393,\n        \"min\": 4.989601238872484,\n        \"max\": 5.704612469838172,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          5.0386897288205725,\n          5.451100205772531,\n          5.183178550884765\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 69
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **08.파이썬 추천 시스템 패키지-Surprise**"
+      ],
+      "metadata": {
+        "id": "8yZkAqfbiT78"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np"
+      ],
+      "metadata": {
+        "id": "UByPgYZVVyxn"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install scikit-surprise"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "moXV4rgAiaVQ",
+        "outputId": "648c3ae2-968d-4081-9229-c38adc41567a"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: scikit-surprise in /usr/local/lib/python3.12/dist-packages (1.1.4)\n",
+            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-surprise) (1.5.3)\n",
+            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.12/dist-packages (from scikit-surprise) (1.26.4)\n",
+            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from scikit-surprise) (1.16.3)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* Surprise에서 데이터 로딩은 Dataset 클래스를 이용해서만 가능.\n",
+        "* Surprise는 무비렌즈 사이트에서 제공하는 과거 버전의 데이터 세트를 가져오는 API를 제공\n",
+        "* Surprise Dataset 클래스의 load_builtin()은 무비렌즈 사이트에서 제공하는 과거 버전\n",
+        "데이터 세트인 ‘ml-10*(10만 개 평점 데이터) 또는 ‘ml-lm’(100만 개 평점 데이터) 데이터를 아카\n",
+        "이브 사이트로부터 내려받아 로컬 디렉터리에 저장한 뒤 데이터를 로딩합니다."
+      ],
+      "metadata": {
+        "id": "MHg0fV8ulD2t"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from surprise import SVD\n",
+        "from surprise import Dataset\n",
+        "from surprise import accuracy\n",
+        "from surprise.model_selection import train_test_split"
+      ],
+      "metadata": {
+        "id": "DiqAwYlhkZNT"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data = Dataset.load_builtin('ml-100k')\n",
+        "#수행 시마다 동일하게 데이터를 분할하기 위해 random_state 값 부여\n",
+        "trainset, testset = train_test_split(data, test_size =.25, random_state=0)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "B_Ws32sEqTS9",
+        "outputId": "38c1a86d-149b-46c1-855f-c23dbe79b240"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Dataset ml-100k could not be found. Do you want to download it? [Y/n] Y\n",
+            "Trying to download dataset from https://files.grouplens.org/datasets/movielens/ml-100k.zip...\n",
+            "Done! Dataset ml-100k has been saved to /root/.surprise_data/ml-100k\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* 과거 버전의 데이터 세트. 분리 문자가 \\t문자\n",
+        "* 무비렌즈 사이트에서 내려받은 데이터 파일과 동이랗게 로우 레벨의 사용자-아이템 평점 데이터를 그대로 적용 과거 버전의 데이터 세트. 분리 문자가 \\t문자\n",
+        "* 무비렌즈 사이트에서 내려받은 데이터 파일과 동이랗게 로우 레벨의 사용자-아이템 평점 데이터를 그대로 적용"
+      ],
+      "metadata": {
+        "id": "wwyFrujWqo1b"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "algo = SVD(random_state=0)\n",
+        "algo.fit(trainset)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "urgvZhtwrX1u",
+        "outputId": "f62c1c61-f25c-48bd-b97c-9817636622e7"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x7f7efdacdeb0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "predictions = algo.test(testset)\n",
+        "print('prediction type:', type(predictions), 'size:', len(predictions))\n",
+        "print('prediction 결과의 최초 5개 추출')\n",
+        "predictions[:5]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "z_GMf6ChrpkA",
+        "outputId": "9d6a036e-32b6-4b23-c551-278f46d6eeda"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "prediction type: <class 'list'> size: 25000\n",
+            "prediction 결과의 최초 5개 추출\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[Prediction(uid='120', iid='282', r_ui=4.0, est=3.5114147666251547, details={'was_impossible': False}),\n",
+              " Prediction(uid='882', iid='291', r_ui=4.0, est=3.573872419581491, details={'was_impossible': False}),\n",
+              " Prediction(uid='535', iid='507', r_ui=5.0, est=4.033583485472447, details={'was_impossible': False}),\n",
+              " Prediction(uid='697', iid='244', r_ui=5.0, est=3.8463639495936905, details={'was_impossible': False}),\n",
+              " Prediction(uid='751', iid='385', r_ui=4.0, est=3.1807542478219157, details={'was_impossible': False})]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "[(pred.uid, pred.iid, pred.est) for pred in predictions[:3]]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "L8vU9v2VsYxG",
+        "outputId": "f070c09f-1c1f-476c-cb25-0534fe50afd2"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[('120', '282', 3.5114147666251547),\n",
+              " ('882', '291', 3.573872419581491),\n",
+              " ('535', '507', 4.033583485472447)]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "uid = str(196)\n",
+        "iid = str(302)\n",
+        "pred = algo.predict(uid, iid)\n",
+        "print(pred)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3LXfM9KfsenL",
+        "outputId": "fdced8ac-e6ce-4688-b758-1090593dc166"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "user: 196        item: 302        r_ui = None   est = 4.49   {'was_impossible': False}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "accuracy.rmse(predictions)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZVUmMYoos1HB",
+        "outputId": "9deb72e2-5db3-4e7b-fdcc-e7dbb61bd4f5"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 0.9467\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9466860806937948"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**OS 파일 데이터를 Surprise 데이터 세트로 로딩**\n",
+        "* 주의: 로딩되는 데이터 파일에 칼럼명을 가지는 헤더 문자열이 있어서는 안됨. -> ratings.csv 파일은 헤더를 가지고 있음.->to_csv() 함수로 헤더 삭제"
+      ],
+      "metadata": {
+        "id": "pB5LFb3bs5zF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "ratings = pd.read_csv('/content/ratings.csv')\n",
+        "#헤더 제거한 새로운 파일 생성\n",
+        "ratings.to_csv('/content/ratings_noh.csv', index = False, header=False)"
+      ],
+      "metadata": {
+        "id": "9K0qLK5DudQ1"
+      },
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* Reader 클래스를 이용해 데이터 파일의 파싱 포맷을 정의\n",
+        "* 4개의 칼럼이 사용자 아이디, 아이템 아이디, 평점, 타임스탬프임을 로딩할 때 알려줘야함"
+      ],
+      "metadata": {
+        "id": "YJC8vh3luzzr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from surprise import Reader\n",
+        "\n",
+        "reader = Reader(line_format='user item rating timestamp', sep = ',', rating_scale = (0.5,5))\n",
+        "data = Dataset.load_from_file('/content/ratings_noh.csv', reader=reader)"
+      ],
+      "metadata": {
+        "id": "G4JqDZCcvC7Z"
+      },
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* Surprise 데이터 세트는 기본적으로 무비렌즈 데이터 형식을 따르므로 무비렌즈 데이터 형식이 아닌\n",
+        "다른 OS 파일의 경우 Reader 클래스를 먼저 설정\n",
+        "**Reader 클래스 주요 파라미터**\n",
+        "* line_format(string): 칼럼을 순서대로 나열. 문자열을 공백으로 분리\n",
+        "* sep(char): 칼럼을 분리하는 분리자. 디폴트: \\t. 판다스에서 입력받을 경우 기재 필요X\n",
+        "* rating_scale(tuple, optional): 평점 값의 최소~최대 평점 설저. 디폴트: (1,5)"
+      ],
+      "metadata": {
+        "id": "P1oYHVz9wQKj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trainset, testset = train_test_split(data, test_size =.25, random_state=0)\n",
+        "\n",
+        "algo = SVD(n_factors = 50, random_state=0)\n",
+        "algo.fit(trainset)\n",
+        "predictions = algo.test(testset)\n",
+        "accuracy.rmse(predictions)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HIxK1R0TxDli",
+        "outputId": "f78f192e-d398-4ab4-a891-f3d79a2b5b29"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 0.8682\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8681952927143516"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "** 판다스 DataFrame에서 Surprise 데이터 세트로 로딩**"
+      ],
+      "metadata": {
+        "id": "2yTvebbex2Ix"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "from surprise import Reader, Dataset\n",
+        "\n",
+        "ratings = pd.read_csv('/content/ratings.csv')\n",
+        "reader = Reader(rating_scale=(0.5, 5.0))\n",
+        "\n",
+        "data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']], reader)\n",
+        "trainset, testset = train_test_split(data, test_size = .25, random_state =0)\n",
+        "\n",
+        "algo = SVD(n_factors = 50, random_state=0)\n",
+        "algo.fit(trainset)\n",
+        "predictions = algo.test(testset)\n",
+        "accuracy.rmse(predictions)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "T4k_EmaPx56x",
+        "outputId": "f601cdc3-b237-4fa3-f252-cd5ff350c1a5"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 0.8682\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8681952927143516"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**교차 검증과 하이퍼 파라미터 튜닝**\n",
+        "* Surprise는 교차 검증과 하이퍼 파라미터 튜닝을 위해 사이킷런과 유사한 cross_validate( )와 GridSearchCV 클래스를 제공"
+      ],
+      "metadata": {
+        "id": "iLmPV9Mt29JF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from surprise.model_selection import cross_validate\n",
+        "\n",
+        "ratings = pd.read_csv('/content/ratings.csv')\n",
+        "reader = Reader(rating_scale = (0.5, 5.0))\n",
+        "data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']], reader)\n",
+        "\n",
+        "algo = SVD(random_state = 0)\n",
+        "cross_validate(algo, data, measures=['RMSE', 'MAE'], cv = 5, verbose= True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "R5HFRwSg3Fz7",
+        "outputId": "e606698a-1374-463e-a17f-7cb3b97841bf"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Evaluating RMSE, MAE of algorithm SVD on 5 split(s).\n",
+            "\n",
+            "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
+            "RMSE (testset)    0.8741  0.8766  0.8670  0.8722  0.8729  0.8726  0.0032  \n",
+            "MAE (testset)     0.6716  0.6727  0.6663  0.6686  0.6729  0.6704  0.0026  \n",
+            "Fit time          1.33    1.72    1.25    1.25    1.28    1.37    0.18    \n",
+            "Test time         0.33    0.10    0.21    0.10    0.22    0.19    0.09    \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'test_rmse': array([0.87413994, 0.87656526, 0.86698849, 0.87217666, 0.87291197]),\n",
+              " 'test_mae': array([0.67158723, 0.67271883, 0.66627016, 0.66860324, 0.6728898 ]),\n",
+              " 'fit_time': (1.3310132026672363,\n",
+              "  1.7168080806732178,\n",
+              "  1.25482177734375,\n",
+              "  1.249457836151123,\n",
+              "  1.277155876159668),\n",
+              " 'test_time': (0.3288121223449707,\n",
+              "  0.10231542587280273,\n",
+              "  0.21318769454956055,\n",
+              "  0.0996389389038086,\n",
+              "  0.22016644477844238)}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from surprise.model_selection import GridSearchCV\n",
+        "\n",
+        "param_grid = {'n_epochs': [20, 40,60], 'n_factors':[50, 100, 200]}\n",
+        "gs = GridSearchCV(SVD, param_grid, measures=['rmse', 'mae'], cv=3)\n",
+        "gs.fit(data)\n",
+        "\n",
+        "print(gs.best_score['rmse'])\n",
+        "print(gs.best_params['rmse'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eq5jlRby37on",
+        "outputId": "8d672828-6f69-457b-9a2d-ceeea0bf4e54"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.877736089495885\n",
+            "{'n_epochs': 20, 'n_factors': 50}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Surprise를 이용한 개인화 영화 추천 시스템 구축**"
+      ],
+      "metadata": {
+        "id": "9PQ4ss695v_D"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#다음 코드는 train_test_split()으로 분리되지 않은 데이터 세트에 fit()을 호출해 오류가 발생\n",
+        "data = Dataset.load_from_df(ratings['userId', 'movieId', 'rating'], reader)\n",
+        "algo = SVD(n_factors = 50, random_state=0)\n",
+        "algo.fit(data)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 569
+        },
+        "id": "Qp0Q5TPI6Dgq",
+        "outputId": "6cbe18b5-1633-4689-fb96-dd05f1439dfe"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "KeyError",
+          "evalue": "('userId', 'movieId', 'rating')",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3805\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3806\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;31mKeyError\u001b[0m: ('userId', 'movieId', 'rating')",
+            "\nThe above exception was the direct cause of the following exception:\n",
+            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-3089583357.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#다음 코드는 train_test_split()으로 분리되지 않은 데이터 세트에 fit()을 호출해 오류가 발생\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_from_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mratings\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'userId'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'movieId'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rating'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0malgo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSVD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_factors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0malgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4101\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4102\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4103\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4104\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3810\u001b[0m             ):\n\u001b[1;32m   3811\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3812\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3813\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3814\u001b[0m             \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyError\u001b[0m: ('userId', 'movieId', 'rating')"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from surprise.dataset import DatasetAutoFolds\n",
+        "\n",
+        "reader = Reader(line_format='user item rating timestamp', sep = ',', rating_scale = (0.5,5))\n",
+        "\n",
+        "data_folds = DatasetAutoFolds(ratings_file = '/content/ratings_noh.csv', reader = reader)\n",
+        "\n",
+        "trainset = data_folds.build_full_trainset()"
+      ],
+      "metadata": {
+        "id": "TuxvxeSp6Xhr"
+      },
+      "execution_count": 18,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "algo = SVD(n_epochs=20, n_factors = 50, random_state=0)\n",
+        "algo.fit(trainset)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "D7t3MOdeuHzf",
+        "outputId": "ff85cfe8-457a-45c4-d674-e062bb21931e"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x7f7edfcac080>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "movies = pd.read_csv('/content/movies.csv')\n",
+        "\n",
+        "movieIds = ratings[ratings['userId'] == 9]['movieId']\n",
+        "\n",
+        "if movieIds[movieIds==42].count() ==0:\n",
+        "  print('사용자 아이디 9는 영화 아이디 42의 평점 없음')\n",
+        "\n",
+        "print(movies[movies['movieId']==42])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "93GKv6Td7EmI",
+        "outputId": "6d8f4660-77d3-4a53-e51c-d33ec3781271"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "사용자 아이디 9는 영화 아이디 42의 평점 없음\n",
+            "    movieId                   title              genres\n",
+            "38       42  Dead Presidents (1995)  Action|Crime|Drama\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "uid = str(9)\n",
+        "iid = str(42)\n",
+        "\n",
+        "pred = algo.predict(uid, iid, verbose=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "a0ps2XQ77hAG",
+        "outputId": "b7d45d82-3e82-4b55-d747-8116e3352790"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "user: 9          item: 42         r_ui = None   est = 3.13   {'was_impossible': False}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_unseen_surprise(ratings, movies, userId):\n",
+        "  seen_movies = ratings[ratings['userId']==userId]['movieId'].tolist()\n",
+        "  total_movies = movies['movieId'].tolist()\n",
+        "\n",
+        "  unseen_movies = [movie for movie in total_movies if movie not in movies]\n",
+        "  print('평점 매긴 영화 수:', len(seen_movies), '추천 대상 영화 수:', len(unseen_movies),\n",
+        "        '전체 영화 수:', len(total_movies))\n",
+        "  return unseen_movies\n",
+        "\n",
+        "unseen_movies = get_unseen_surprise(ratings, movies, 9)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SVL_7m017o_u",
+        "outputId": "906a4459-d4c2-43a2-89e0-6565e5d688cd"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "평점 매긴 영화 수: 46 추천 대상 영화 수: 9742 전체 영화 수: 9742\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def recomm_movie_by_surprise(algo, userId, unseen_movies, top_n=10):\n",
+        "  predictions = [algo.predict(str(userId), str(movieId)) for movieId in unseen_movies]\n",
+        "\n",
+        "  def sortkey_est(pred):\n",
+        "    return pred.est\n",
+        "  predictions.sort(key=sortkey_est, reverse=True)\n",
+        "  top_predictions = predictions[:top_n]\n",
+        "\n",
+        "  top_movie_ids = [int(pred.iid) for pred in top_predictions]\n",
+        "  top_movie_rating = [pred.est for pred in top_predictions]\n",
+        "  top_movie_titles = movies[movies.movieId.isin(top_movie_ids)]['title']\n",
+        "  top_movie_preds = [(id, title, rating) for id, title, rating in zip(top_movie_ids, top_movie_titles, top_movie_rating)]\n",
+        "\n",
+        "  return top_movie_preds\n",
+        "\n",
+        "unseen_movies = get_unseen_surprise(ratings, movies, 9)\n",
+        "top_movie_preds = recomm_movie_by_surprise(algo, 9, unseen_movies, top_n=10)\n",
+        "\n",
+        "print('#### Top-10 추천 영화 리스트 ####')\n",
+        "for top_movie in top_movie_preds:\n",
+        "  print(top_movie[1], \":\", top_movie[2])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ed_wXo3I8z6A",
+        "outputId": "ec468318-ef81-4471-d974-0d609d1b1444"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "평점 매긴 영화 수: 46 추천 대상 영화 수: 9742 전체 영화 수: 9742\n",
+            "#### Top-10 추천 영화 리스트 ####\n",
+            "Usual Suspects, The (1995) : 4.306302135700814\n",
+            "Star Wars: Episode IV - A New Hope (1977) : 4.281663842987387\n",
+            "Pulp Fiction (1994) : 4.278152632122759\n",
+            "Godfather, The (1972) : 4.226073566460876\n",
+            "Streetcar Named Desire, A (1951) : 4.205267497432363\n",
+            "Star Wars: Episode V - The Empire Strikes Back (1980) : 4.1918097904381995\n",
+            "Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981) : 4.154746591122657\n",
+            "Star Wars: Episode VI - Return of the Jedi (1983) : 4.122016128534504\n",
+            "Goodfellas (1990) : 4.118002684813024\n",
+            "Lord of the Rings: The Fellowship of the Ring, The (2001) : 4.108009609093436\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file