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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "a6cd2625",
+      "metadata": {
+        "id": "a6cd2625"
+      },
+      "source": [
+        "# Wine Quality\n",
+        "#### Nombre del dataset :  [Wine Quality del UCI Machine Learning Repository](https://archive.ics.uci.edu/dataset/186/wine+quality)\n",
+        "\n",
+        "\n",
+        "### Introducción\n",
+        "\n",
+        "Este proyecto se centra en el análisis exploratorio de datos (EDA) de un conjunto de datos de calidad del vino, que contiene información sobre vinos rojos y blancos.\n",
+        "\n",
+        "### Origen de los Datos\n",
+        "\n",
+        "Los datos utilizados en este análisis provienen del Repositorio de Aprendizaje Automático de la UCI (Universidad de California, Irvine). Fueron recopilados por P. Cortez, A. Cerdeira, F. Almeida, T. Matos y J. Reis en la Universidade de Minho, Portugal. Estos investigadores recopilaron información sobre diferentes propiedades químicas de vinos y la calidad percibida por catadores expertos.\n",
+        "\n",
+        "###  Objetivo del Análisis\n",
+        "\n",
+        "El objetivo principal de este análisis es comprender en profundidad el conjunto de datos y extraer información valiosa que pueda ser útil para desarrollar modelos de aprendizaje automático para predecir la calidad del vino en función de sus propiedades químicas. Además de este objetivo central, existen otros objetivos más específicos, que incluyen:\n",
+        "\n",
+        "1. Identificar patrones y relaciones entre las características químicas y la calidad del vino.\n",
+        "2. Evaluar la distribución de la calidad del vino.\n",
+        "3. Realizar un análisis exploratorio para revelar insights y tendencias interesantes.\n",
+        "\n",
+        "####  Variables en el Conjunto de Datos\n",
+        "\n",
+        "El conjunto de datos se divide en dos archivos CSV: uno para vinos rojos y otro para vinos blancos. Cada archivo contiene las siguientes variables con sus respectivas unidades:\n",
+        "\n",
+        "####  Variables de Características Químicas (Entradas):\n",
+        "\n",
+        "1. `fixed acidity` (g/dm³): Acidez fija.\n",
+        "2. `volatile acidity` (g/dm³): Acidez volátil.\n",
+        "3. `citric acid` (g/dm³): Ácido cítrico.\n",
+        "4. `residual sugar` (g/dm³): Azúcar residual.\n",
+        "5. `chlorides` (g/dm³): Cloruros.\n",
+        "6. `free sulfur dioxide` (mg/dm³): Dióxido de azufre libre.\n",
+        "7. `total sulfur dioxide` (mg/dm³): Dióxido de azufre total.\n",
+        "8. `density` (g/cm³): Densidad.\n",
+        "9. `pH`: pH.\n",
+        "10. `sulphates` (g/dm³): Sulfatos.\n",
+        "11. `alcohol` (% vol): Contenido de alcohol.\n",
+        "\n",
+        "### Variable de Calidad (Objetivo):\n",
+        "\n",
+        "12. `quality`: Calidad del vino (puntuación de 0 a 10, donde 0 es de muy mala calidad y 10 es excelente de calidad).\n",
+        "\n",
+        "### Análisis Exploratorio de Datos - Conjunto de Datos de Calidad del Vino\n",
+        "\n",
+        "En las secciones siguientes de este notebook, realizaremos un análisis exhaustivo de estas variables y exploraremos relaciones, tendencias y patrones. Además, se llevarán a cabo visualizaciones gráficas para facilitar la comprensión de los datos."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "311bce01",
+      "metadata": {
+        "id": "311bce01"
+      },
+      "outputs": [],
+      "source": [
+        "# Importación de librerías para tratamiento de datos\n",
+        "# ==============================================================================\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# Importación de librerías para gráficos\n",
+        "# ==============================================================================\n",
+        "import matplotlib.pyplot as plt\n",
+        "from matplotlib import style\n",
+        "import seaborn as sns\n",
+        "import statsmodels.api as sm\n",
+        "from tabulate import tabulate\n",
+        "\n",
+        "# Importación de librerías para estadística descriptiva\n",
+        "# ==============================================================================\n",
+        "from scipy.stats import shapiro, kstest, anderson\n",
+        "from scipy.stats import describe\n",
+        "\n",
+        "# Importación de librerías para métricas y evaluación\n",
+        "# ==============================================================================\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.feature_selection import SelectFromModel\n",
+        "from sklearn.model_selection import KFold\n",
+        "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
+        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
+        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+        "\n",
+        "# Importación de librerías para modelado\n",
+        "# ==============================================================================\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
+        "from sklearn.naive_bayes import GaussianNB\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.svm import SVC\n",
+        "from sklearn.svm import SVR"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "3c4f1f25",
+      "metadata": {
+        "id": "3c4f1f25"
+      },
+      "outputs": [],
+      "source": [
+        "# Nombres de las columnas (variables)\n",
+        "column_names = [\"fixed acidity\", \"volatile acidity\", \"citric acid\", \"residual sugar\", \"chlorides\",\n",
+        "                \"free sulfur dioxide\", \"total sulfur dioxide\", \"density\", \"pH\", \"sulphates\", \"alcohol\", \"quality\"]\n",
+        "\n",
+        "# Datos de vinos rojos\n",
+        "red_wine_df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\", sep=\";\", names=column_names, header=0)\n",
+        "\n",
+        "# Datos de vinos blancos\n",
+        "white_wine_df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv\", sep=\";\", names=column_names, header=0)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "49798165",
+      "metadata": {
+        "id": "49798165"
+      },
+      "source": [
+        "### Vinos Blancos (White Wine)\n",
+        "1. **Aspectos Generales**:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "003b9de3",
+      "metadata": {
+        "id": "003b9de3"
+      },
+      "outputs": [],
+      "source": [
+        "X = white_wine_df.drop(\"quality\", axis=1)\n",
+        "y = white_wine_df[\"quality\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "ce12f514",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 458
+        },
+        "id": "ce12f514",
+        "outputId": "3e3134aa-db16-4c32-84e9-e156c0967833"
+      },
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+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+              "0               7.0              0.27         0.36            20.7      0.045   \n",
+              "1               6.3              0.30         0.34             1.6      0.049   \n",
+              "2               8.1              0.28         0.40             6.9      0.050   \n",
+              "3               7.2              0.23         0.32             8.5      0.058   \n",
+              "4               7.2              0.23         0.32             8.5      0.058   \n",
+              "...             ...               ...          ...             ...        ...   \n",
+              "4893            6.2              0.21         0.29             1.6      0.039   \n",
+              "4894            6.6              0.32         0.36             8.0      0.047   \n",
+              "4895            6.5              0.24         0.19             1.2      0.041   \n",
+              "4896            5.5              0.29         0.30             1.1      0.022   \n",
+              "4897            6.0              0.21         0.38             0.8      0.020   \n",
+              "\n",
+              "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
+              "0                    45.0                 170.0  1.00100  3.00       0.45   \n",
+              "1                    14.0                 132.0  0.99400  3.30       0.49   \n",
+              "2                    30.0                  97.0  0.99510  3.26       0.44   \n",
+              "3                    47.0                 186.0  0.99560  3.19       0.40   \n",
+              "4                    47.0                 186.0  0.99560  3.19       0.40   \n",
+              "...                   ...                   ...      ...   ...        ...   \n",
+              "4893                 24.0                  92.0  0.99114  3.27       0.50   \n",
+              "4894                 57.0                 168.0  0.99490  3.15       0.46   \n",
+              "4895                 30.0                 111.0  0.99254  2.99       0.46   \n",
+              "4896                 20.0                 110.0  0.98869  3.34       0.38   \n",
+              "4897                 22.0                  98.0  0.98941  3.26       0.32   \n",
+              "\n",
+              "      alcohol  quality  \n",
+              "0         8.8        6  \n",
+              "1         9.5        6  \n",
+              "2        10.1        6  \n",
+              "3         9.9        6  \n",
+              "4         9.9        6  \n",
+              "...       ...      ...  \n",
+              "4893     11.2        6  \n",
+              "4894      9.6        5  \n",
+              "4895      9.4        6  \n",
+              "4896     12.8        7  \n",
+              "4897     11.8        6  \n",
+              "\n",
+              "[4898 rows x 12 columns]"
+            ],
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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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+              "\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>fixed acidity</th>\n",
+              "      <th>volatile acidity</th>\n",
+              "      <th>citric acid</th>\n",
+              "      <th>residual sugar</th>\n",
+              "      <th>chlorides</th>\n",
+              "      <th>free sulfur dioxide</th>\n",
+              "      <th>total sulfur dioxide</th>\n",
+              "      <th>density</th>\n",
+              "      <th>pH</th>\n",
+              "      <th>sulphates</th>\n",
+              "      <th>alcohol</th>\n",
+              "      <th>quality</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>7.0</td>\n",
+              "      <td>0.27</td>\n",
+              "      <td>0.36</td>\n",
+              "      <td>20.7</td>\n",
+              "      <td>0.045</td>\n",
+              "      <td>45.0</td>\n",
+              "      <td>170.0</td>\n",
+              "      <td>1.00100</td>\n",
+              "      <td>3.00</td>\n",
+              "      <td>0.45</td>\n",
+              "      <td>8.8</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>6.3</td>\n",
+              "      <td>0.30</td>\n",
+              "      <td>0.34</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>0.049</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>132.0</td>\n",
+              "      <td>0.99400</td>\n",
+              "      <td>3.30</td>\n",
+              "      <td>0.49</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>8.1</td>\n",
+              "      <td>0.28</td>\n",
+              "      <td>0.40</td>\n",
+              "      <td>6.9</td>\n",
+              "      <td>0.050</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>97.0</td>\n",
+              "      <td>0.99510</td>\n",
+              "      <td>3.26</td>\n",
+              "      <td>0.44</td>\n",
+              "      <td>10.1</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>7.2</td>\n",
+              "      <td>0.23</td>\n",
+              "      <td>0.32</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>0.058</td>\n",
+              "      <td>47.0</td>\n",
+              "      <td>186.0</td>\n",
+              "      <td>0.99560</td>\n",
+              "      <td>3.19</td>\n",
+              "      <td>0.40</td>\n",
+              "      <td>9.9</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>7.2</td>\n",
+              "      <td>0.23</td>\n",
+              "      <td>0.32</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>0.058</td>\n",
+              "      <td>47.0</td>\n",
+              "      <td>186.0</td>\n",
+              "      <td>0.99560</td>\n",
+              "      <td>3.19</td>\n",
+              "      <td>0.40</td>\n",
+              "      <td>9.9</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4893</th>\n",
+              "      <td>6.2</td>\n",
+              "      <td>0.21</td>\n",
+              "      <td>0.29</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>0.039</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>92.0</td>\n",
+              "      <td>0.99114</td>\n",
+              "      <td>3.27</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>11.2</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4894</th>\n",
+              "      <td>6.6</td>\n",
+              "      <td>0.32</td>\n",
+              "      <td>0.36</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>0.047</td>\n",
+              "      <td>57.0</td>\n",
+              "      <td>168.0</td>\n",
+              "      <td>0.99490</td>\n",
+              "      <td>3.15</td>\n",
+              "      <td>0.46</td>\n",
+              "      <td>9.6</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4895</th>\n",
+              "      <td>6.5</td>\n",
+              "      <td>0.24</td>\n",
+              "      <td>0.19</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.041</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>111.0</td>\n",
+              "      <td>0.99254</td>\n",
+              "      <td>2.99</td>\n",
+              "      <td>0.46</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4896</th>\n",
+              "      <td>5.5</td>\n",
+              "      <td>0.29</td>\n",
+              "      <td>0.30</td>\n",
+              "      <td>1.1</td>\n",
+              "      <td>0.022</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>0.98869</td>\n",
+              "      <td>3.34</td>\n",
+              "      <td>0.38</td>\n",
+              "      <td>12.8</td>\n",
+              "      <td>7</td>\n",
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+              "    <tr>\n",
+              "      <th>4897</th>\n",
+              "      <td>6.0</td>\n",
+              "      <td>0.21</td>\n",
+              "      <td>0.38</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>0.020</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>98.0</td>\n",
+              "      <td>0.98941</td>\n",
+              "      <td>3.26</td>\n",
+              "      <td>0.32</td>\n",
+              "      <td>11.8</td>\n",
+              "      <td>6</td>\n",
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+              "        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-b6baac47-0511-45b2-9d2c-5d0c57e77bdb\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b6baac47-0511-45b2-9d2c-5d0c57e77bdb')\"\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-b6baac47-0511-45b2-9d2c-5d0c57e77bdb button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_6724d9d2-6369-4ffb-bc17-8cb64fab43f8\">\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('white_wine_df')\"\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_6724d9d2-6369-4ffb-bc17-8cb64fab43f8 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('white_wine_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ],
+      "source": [
+        "#Visualización del dataframe\n",
+        "white_wine_df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "ef49b39b",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ef49b39b",
+        "outputId": "f6557d20-a320-44c0-8656-982f43a1f10e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(4898, 12)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "#Forma del dataframe\n",
+        "white_wine_df.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "46ed4c47",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "46ed4c47",
+        "outputId": "bd2f6961-6741-4758-a20a-fa6015408825"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Index(['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
+            "       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
+            "       'pH', 'sulphates', 'alcohol', 'quality'],\n",
+            "      dtype='object')\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(white_wine_df.columns)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "9e7f9f80",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9e7f9f80",
+        "outputId": "96b8e1f7-eeb1-45c5-a79e-547a6d2ed709"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 4898 entries, 0 to 4897\n",
+            "Data columns (total 12 columns):\n",
+            " #   Column                Non-Null Count  Dtype  \n",
+            "---  ------                --------------  -----  \n",
+            " 0   fixed acidity         4898 non-null   float64\n",
+            " 1   volatile acidity      4898 non-null   float64\n",
+            " 2   citric acid           4898 non-null   float64\n",
+            " 3   residual sugar        4898 non-null   float64\n",
+            " 4   chlorides             4898 non-null   float64\n",
+            " 5   free sulfur dioxide   4898 non-null   float64\n",
+            " 6   total sulfur dioxide  4898 non-null   float64\n",
+            " 7   density               4898 non-null   float64\n",
+            " 8   pH                    4898 non-null   float64\n",
+            " 9   sulphates             4898 non-null   float64\n",
+            " 10  alcohol               4898 non-null   float64\n",
+            " 11  quality               4898 non-null   int64  \n",
+            "dtypes: float64(11), int64(1)\n",
+            "memory usage: 459.3 KB\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Aspectos básicos del DataFrame\n",
+        "white_wine_df.info()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "c22ae7e8",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "c22ae7e8",
+        "outputId": "b12f4c57-48df-42ab-bfd1-7305bb0bf4d4"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "fixed acidity           0\n",
+              "volatile acidity        0\n",
+              "citric acid             0\n",
+              "residual sugar          0\n",
+              "chlorides               0\n",
+              "free sulfur dioxide     0\n",
+              "total sulfur dioxide    0\n",
+              "density                 0\n",
+              "pH                      0\n",
+              "sulphates               0\n",
+              "alcohol                 0\n",
+              "quality                 0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ],
+      "source": [
+        "#Examinando datos perdidos\n",
+        "white_wine_df.isna().sum()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "b96c553f",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "b96c553f",
+        "outputId": "4b9ffa8f-a325-40f7-f0ae-6dca228f4b0d"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.hist(y, bins=10, edgecolor='k')\n",
+        "plt.xlabel('Calidad del Vino')\n",
+        "plt.ylabel('Frecuencia')\n",
+        "plt.title('Distribución de Calidad del Vino')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d316f8eb",
+      "metadata": {
+        "id": "d316f8eb"
+      },
+      "source": [
+        "2. **Estadistica básica: Normalidad del conjuto de datos**:\n",
+        "\n",
+        " 2.1 Una forma visual preliminar de verificar la normalidad de tus datos es crear un **histograma**. Si la distribución se asemeja a una campana es simétrica, en este caso lo más probable es que siga una distribución normal, por la forma de campana. De igual manera vemos que tiene leves asimetrias, por lo cual vamos utilizar otros metodos para comprobar su distribución."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "724ae45a",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 982
+        },
+        "id": "724ae45a",
+        "outputId": "b129f97e-b159-4977-c2a2-83b079307341"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1400x1400 with 12 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "#Construcción de histogramas\n",
+        "import seaborn as sns\n",
+        "\n",
+        "fig, axes = plt.subplots(4, 3, figsize=(14, 14))\n",
+        "\n",
+        "for i, column in enumerate(column_names[:-1]):\n",
+        "    row = i // 3  # Índice de fila\n",
+        "    col = i % 3   # Índice de columna\n",
+        "    sns.histplot(white_wine_df[column], kde=True, ax=axes[row, col], color= (list(plt.rcParams['axes.prop_cycle'])*2)[i][\"color\"])\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e72636f5",
+      "metadata": {
+        "id": "e72636f5"
+      },
+      "source": [
+        " 2.2 _Pruebas de distribución normal_:\n",
+        "Para determinar si el conjunto de datos sigue una distribución normal, utilizaremos la prueba de Anderson-Darling. Esta prueba es más efectiva para detectar desviaciones de la normalidad en las colas largas de la distribución, como se puede observar en los histogramas. Esto significa que puede identificar valores atípicos en las colas con mayor eficacia que la Prueba de Shapiro-Wilk. Además, la Prueba de Shapiro-Wilk funciona bien con tamaños de muestra generalmente inferiores a 2.000 observaciones, mientras que para el conjunto de datos del vino blanco contamos con 4.898 entradas.\n",
+        "\n",
+        "Hipótesis nula (H0): Los datos siguen una distribución normal o gaussiana.\n",
+        "\n",
+        "Hipótesis alternativa (Ha): Los datos no siguen una distribución normal"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "742fb977",
+      "metadata": {
+        "scrolled": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 425
+        },
+        "id": "742fb977",
+        "outputId": "e3080098-013a-4e0d-b28c-923cbffc578c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                 Feature  Anderson-Darling p-value Anderson-Darling Normality  \\\n",
+              "0          fixed acidity                     0.786            Se acepta la H0   \n",
+              "1       volatile acidity                     0.786            Se acepta la H0   \n",
+              "2            citric acid                     0.786            Se acepta la H0   \n",
+              "3         residual sugar                     0.786            Se acepta la H0   \n",
+              "4              chlorides                     0.786            Se acepta la H0   \n",
+              "5    free sulfur dioxide                     0.786            Se acepta la H0   \n",
+              "6   total sulfur dioxide                     0.786            Se acepta la H0   \n",
+              "7                density                     0.786            Se acepta la H0   \n",
+              "8                     pH                     0.786            Se acepta la H0   \n",
+              "9              sulphates                     0.786            Se acepta la H0   \n",
+              "10               alcohol                     0.786            Se acepta la H0   \n",
+              "11               quality                     0.786            Se acepta la H0   \n",
+              "\n",
+              "    Skewness Skewness Interpretation  \n",
+              "0   0.647553      Sesgo a la derecha  \n",
+              "1   1.576497      Sesgo a la derecha  \n",
+              "2   1.281528      Sesgo a la derecha  \n",
+              "3   1.076764      Sesgo a la derecha  \n",
+              "4   5.021792      Sesgo a la derecha  \n",
+              "5   1.406314      Sesgo a la derecha  \n",
+              "6   0.390590      Sesgo a la derecha  \n",
+              "7   0.977474      Sesgo a la derecha  \n",
+              "8   0.457642      Sesgo a la derecha  \n",
+              "9   0.976894      Sesgo a la derecha  \n",
+              "10  0.487193      Sesgo a la derecha  \n",
+              "11  0.155749      Sesgo a la derecha  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f6917373-c698-4c2e-be93-8d6c5c53192e\" 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>Feature</th>\n",
+              "      <th>Anderson-Darling p-value</th>\n",
+              "      <th>Anderson-Darling Normality</th>\n",
+              "      <th>Skewness</th>\n",
+              "      <th>Skewness Interpretation</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>fixed acidity</td>\n",
+              "      <td>0.786</td>\n",
+              "      <td>Se acepta la H0</td>\n",
+              "      <td>0.647553</td>\n",
+              "      <td>Sesgo a la derecha</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>volatile acidity</td>\n",
+              "      <td>0.786</td>\n",
+              "      <td>Se acepta la H0</td>\n",
+              "      <td>1.576497</td>\n",
+              "      <td>Sesgo a la derecha</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>citric acid</td>\n",
+              "      <td>0.786</td>\n",
+              "      <td>Se acepta la H0</td>\n",
+              "      <td>1.281528</td>\n",
+              "      <td>Sesgo a la derecha</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>residual sugar</td>\n",
+              "      <td>0.786</td>\n",
+              "      <td>Se acepta la H0</td>\n",
+              "      <td>1.076764</td>\n",
+              "      <td>Sesgo a la derecha</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>chlorides</td>\n",
+              "      <td>0.786</td>\n",
+              "      <td>Se acepta la H0</td>\n",
+              "      <td>5.021792</td>\n",
+              "      <td>Sesgo a la derecha</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>free sulfur dioxide</td>\n",
+              "      <td>0.786</td>\n",
+              "      <td>Se acepta la H0</td>\n",
+              "      <td>1.406314</td>\n",
+              "      <td>Sesgo a la derecha</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>total sulfur dioxide</td>\n",
+              "      <td>0.786</td>\n",
+              "      <td>Se acepta la H0</td>\n",
+              "      <td>0.390590</td>\n",
+              "      <td>Sesgo a la derecha</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>density</td>\n",
+              "      <td>0.786</td>\n",
+              "      <td>Se acepta la H0</td>\n",
+              "      <td>0.977474</td>\n",
+              "      <td>Sesgo a la derecha</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>pH</td>\n",
+              "      <td>0.786</td>\n",
+              "      <td>Se acepta la H0</td>\n",
+              "      <td>0.457642</td>\n",
+              "      <td>Sesgo a la derecha</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>sulphates</td>\n",
+              "      <td>0.786</td>\n",
+              "      <td>Se acepta la H0</td>\n",
+              "      <td>0.976894</td>\n",
+              "      <td>Sesgo a la derecha</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>alcohol</td>\n",
+              "      <td>0.786</td>\n",
+              "      <td>Se acepta la H0</td>\n",
+              "      <td>0.487193</td>\n",
+              "      <td>Sesgo a la derecha</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>quality</td>\n",
+              "      <td>0.786</td>\n",
+              "      <td>Se acepta la H0</td>\n",
+              "      <td>0.155749</td>\n",
+              "      <td>Sesgo a la derecha</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-f6917373-c698-4c2e-be93-8d6c5c53192e')\"\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-f6917373-c698-4c2e-be93-8d6c5c53192e 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-f6917373-c698-4c2e-be93-8d6c5c53192e');\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-a0927fe7-6793-4399-be0b-4f4d47b51dce\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a0927fe7-6793-4399-be0b-4f4d47b51dce')\"\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-a0927fe7-6793-4399-be0b-4f4d47b51dce button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_07bed036-03c2-435c-a7df-0d6d3101a7e9\">\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('results')\"\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_07bed036-03c2-435c-a7df-0d6d3101a7e9 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('results');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ],
+      "source": [
+        "# DataFrame para almacenar los resultados\n",
+        "results = pd.DataFrame(columns=[\"Feature\", \"Anderson-Darling p-value\", \"Anderson-Darling Normality\", \"Skewness\", \"Skewness Interpretation\"])\n",
+        "\n",
+        "# nivel de significancia\n",
+        "alpha = 0.05\n",
+        "\n",
+        "for feature in column_names:\n",
+        "    data = white_wine_df[feature]\n",
+        "\n",
+        "    # Prueba de Anderson-Darling\n",
+        "    anderson_result = anderson(data)\n",
+        "    ad_p = anderson_result.critical_values[2]\n",
+        "    if ad_p > alpha:\n",
+        "        ad_normality = \"Se acepta la H0\"\n",
+        "    else:\n",
+        "        ad_normality = \"Se rechaza la H0\"\n",
+        "\n",
+        "    # Estadísticas descriptivas\n",
+        "    stats = describe(data)\n",
+        "\n",
+        "    #  estadísticas de sesgo\n",
+        "    skewness = stats.skewness\n",
+        "    skewness_interpretation = \"Sesgo a la izquierda\" if skewness < 0 else \"Sesgo a la derecha\" if skewness > 0 else \"Sin sesgo\"\n",
+        "\n",
+        "    # Agregar los resultados al DataFrame\n",
+        "    result_df = pd.DataFrame({\n",
+        "        \"Feature\": [feature],\n",
+        "        \"Anderson-Darling p-value\": [ad_p],\n",
+        "        \"Anderson-Darling Normality\": [ad_normality],\n",
+        "        \"Skewness\": [skewness],\n",
+        "        \"Skewness Interpretation\": [skewness_interpretation]\n",
+        "    })\n",
+        "    results = pd.concat([results, result_df], ignore_index=True)\n",
+        "\n",
+        "results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "s98D2ni9wrx4"
+      },
+      "id": "s98D2ni9wrx4",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Los **resultados** de la Evaluación de Normalidad indicaron que se encontraron distribuciones normales, lo cual respalda la aceptación de la hipótesis nula."
+      ],
+      "metadata": {
+        "id": "Sak_2MMRveG7"
+      },
+      "id": "Sak_2MMRveG7"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "74874889",
+      "metadata": {
+        "id": "74874889"
+      },
+      "source": [
+        "2. 3 _Matriz de correlación_:\n",
+        "Realizamos esta tarea para descubrir las posibles conexiones entre las variables y entender cómo pueden llegar a afectar el desempeño del algoritmo. Esto nos ayuda a elegir las características de manera más eficaz, identificar y reducir posibles problemas de multicolinealidad, y mejorar en general la configuración del modelo."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "e96f442b",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 840
+        },
+        "id": "e96f442b",
+        "outputId": "c715efc5-af2e-4a7f-ecc7-6e869531b93e"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x800 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import seaborn as sns\n",
+        "\n",
+        "# matriz de correlación de variables independientes\n",
+        "x_correlation_matrix = X.corr()\n",
+        "\n",
+        "\n",
+        "plt.figure(figsize=(8, 8))\n",
+        "sns.heatmap(x_correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
+        "plt.title(\"Matriz de correlación de variables Independientes\", fontsize=14)\n",
+        "plt.xticks(fontsize=10)\n",
+        "plt.yticks(fontsize=10)\n",
+        "\n",
+        "# los ejes x e y\n",
+        "plt.xlabel(\"Variables\", fontsize=12)\n",
+        "plt.ylabel(\"Variables\", fontsize=12)\n",
+        "\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "bacfa926",
+      "metadata": {
+        "scrolled": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bacfa926",
+        "outputId": "c32835f1-dee4-4bbd-f5c0-93811e0f8037"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "╒══════════════════════╤══════════════════════╤═══════════════╤══════════════════════╕\n",
+            "│ Atributo 1           │ Atributo 2           │   Correlación │ Clasificación        │\n",
+            "╞══════════════════════╪══════════════════════╪═══════════════╪══════════════════════╡\n",
+            "│ fixed acidity        │ pH                   │     -0.425858 │ Moderada correlación │\n",
+            "├──────────────────────┼──────────────────────┼───────────────┼──────────────────────┤\n",
+            "│ residual sugar       │ total sulfur dioxide │      0.401439 │ Moderada correlación │\n",
+            "├──────────────────────┼──────────────────────┼───────────────┼──────────────────────┤\n",
+            "│ residual sugar       │ density              │      0.838966 │ Alta correlación     │\n",
+            "├──────────────────────┼──────────────────────┼───────────────┼──────────────────────┤\n",
+            "│ residual sugar       │ alcohol              │     -0.450631 │ Moderada correlación │\n",
+            "├──────────────────────┼──────────────────────┼───────────────┼──────────────────────┤\n",
+            "│ chlorides            │ alcohol              │     -0.360189 │ Moderada correlación │\n",
+            "├──────────────────────┼──────────────────────┼───────────────┼──────────────────────┤\n",
+            "│ free sulfur dioxide  │ total sulfur dioxide │      0.615501 │ Moderada correlación │\n",
+            "├──────────────────────┼──────────────────────┼───────────────┼──────────────────────┤\n",
+            "│ total sulfur dioxide │ residual sugar       │      0.401439 │ Moderada correlación │\n",
+            "├──────────────────────┼──────────────────────┼───────────────┼──────────────────────┤\n",
+            "│ total sulfur dioxide │ free sulfur dioxide  │      0.615501 │ Moderada correlación │\n",
+            "├──────────────────────┼──────────────────────┼───────────────┼──────────────────────┤\n",
+            "│ total sulfur dioxide │ density              │      0.529881 │ Moderada correlación │\n",
+            "├──────────────────────┼──────────────────────┼───────────────┼──────────────────────┤\n",
+            "│ total sulfur dioxide │ alcohol              │     -0.448892 │ Moderada correlación │\n",
+            "├──────────────────────┼──────────────────────┼───────────────┼──────────────────────┤\n",
+            "│ density              │ residual sugar       │      0.838966 │ Alta correlación     │\n",
+            "├──────────────────────┼──────────────────────┼───────────────┼──────────────────────┤\n",
+            "│ density              │ total sulfur dioxide │      0.529881 │ Moderada correlación │\n",
+            "├──────────────────────┼──────────────────────┼───────────────┼──────────────────────┤\n",
+            "│ density              │ alcohol              │     -0.780138 │ Alta correlación     │\n",
+            "├──────────────────────┼──────────────────────┼───────────────┼──────────────────────┤\n",
+            "│ pH                   │ fixed acidity        │     -0.425858 │ Moderada correlación │\n",
+            "├──────────────────────┼──────────────────────┼───────────────┼──────────────────────┤\n",
+            "│ alcohol              │ residual sugar       │     -0.450631 │ Moderada correlación │\n",
+            "├──────────────────────┼──────────────────────┼───────────────┼──────────────────────┤\n",
+            "│ alcohol              │ chlorides            │     -0.360189 │ Moderada correlación │\n",
+            "├──────────────────────┼──────────────────────┼───────────────┼──────────────────────┤\n",
+            "│ alcohol              │ total sulfur dioxide │     -0.448892 │ Moderada correlación │\n",
+            "├──────────────────────┼──────────────────────┼───────────────┼──────────────────────┤\n",
+            "│ alcohol              │ density              │     -0.780138 │ Alta correlación     │\n",
+            "╘══════════════════════╧══════════════════════╧═══════════════╧══════════════════════╛\n"
+          ]
+        }
+      ],
+      "source": [
+        "classification = []\n",
+        "\n",
+        "for attribute in x_correlation_matrix.columns:\n",
+        "    for other_attribute, correlation in x_correlation_matrix[attribute].items():\n",
+        "        if attribute != other_attribute:\n",
+        "            if abs(correlation)> 0.75:\n",
+        "                classification.append((attribute, other_attribute, correlation, \"Alta correlación\"))\n",
+        "            elif abs(correlation) >0.3:\n",
+        "                classification.append((attribute, other_attribute, correlation, \"Moderada correlación\"))\n",
+        "\n",
+        "headers = [\"Atributo 1\", \"Atributo 2\", \"Correlación\", \"Clasificación\"]\n",
+        "table = tabulate(classification, headers=headers, tablefmt=\"fancy_grid\")\n",
+        "print(table)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e23f6d25",
+      "metadata": {
+        "id": "e23f6d25"
+      },
+      "source": [
+        "**Conclusión** : Se han identificado un par de variables con correlaciones altas, indicando una posible multicolinealidad. Una estrategia para abordar esto es eliminar una de las variables. En este caso, \"residual sugar\" y \"density\" muestran una correlación estadísticamente significativa. No obstante, antes de proceder con la eliminación de alguna de las variables, es importante entender la relevancia de cada variable dentro del problema. Ya que en ocasiones, estas variables pueden proporcionar información única y ser valiosas para el aprendizaje del modelo. Para evaluar esto, llevaremos a cabo un análisis de importancia de características o consideraremos el conocimiento del dominio."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "7553e929",
+      "metadata": {
+        "id": "7553e929"
+      },
+      "outputs": [],
+      "source": [
+        "# Datos en conjuntos de entrenamiento y prueba (80% entrenamiento, 20% prueba)\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "64a07679",
+      "metadata": {
+        "id": "64a07679"
+      },
+      "source": [
+        "2. 5 _Análisis de importancia de características_:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "fa07be18",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fa07be18",
+        "outputId": "c6376b13-978c-42db-eb27-20758c131a0e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Feature: alcohol, Importance: 0.1132\n",
+            "Feature: density, Importance: 0.1064\n",
+            "Feature: volatile acidity, Importance: 0.1020\n",
+            "Feature: free sulfur dioxide, Importance: 0.0946\n",
+            "Feature: total sulfur dioxide, Importance: 0.0914\n",
+            "Feature: residual sugar, Importance: 0.0890\n",
+            "Feature: pH, Importance: 0.0846\n",
+            "Feature: chlorides, Importance: 0.0827\n",
+            "Feature: citric acid, Importance: 0.0808\n",
+            "Feature: sulphates, Importance: 0.0787\n",
+            "Feature: fixed acidity, Importance: 0.0766\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Crea un modelo Random Forest Classifier\n",
+        "clf = RandomForestClassifier(random_state=42)\n",
+        "\n",
+        "# Ajusta el modelo\n",
+        "clf.fit(X_train, y_train)\n",
+        "\n",
+        "# puntuaciones de importancia\n",
+        "importances = clf.feature_importances_\n",
+        "\n",
+        "# importancias con los nombres de las características\n",
+        "feature_importance = list(zip(X.columns, importances))\n",
+        "\n",
+        "# Ordena por su importancia\n",
+        "feature_importance = sorted(feature_importance, key=lambda x: x[1], reverse=True)\n",
+        "\n",
+        "#características y sus puntuaciones de importancia\n",
+        "for feature, importance in feature_importance:\n",
+        "    print(f\"Feature: {feature}, Importance: {importance:.4f}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4e868884",
+      "metadata": {
+        "id": "4e868884"
+      },
+      "source": [
+        "**Conclusión**:\n",
+        "La **importancia de las características** nos indica qué tan influyentes son las características en el proceso de clasificación del modelo. Cuanto mayor sea el valor, más influencia tiene esa característica o atributo dentro del mismo, los resultados nos arrojaron:\n",
+        "\n",
+        "- **Alcohol** es la característica más importante, con una importancia de aproximadamente 0.1132. Se concluye que el alcohol es la característica más influyente en la clasificación.\n",
+        "- **Density** es la segunda característica más importante, con una importancia de aproximadamente 0.1064.\n",
+        "- **Volatile acidity** es la tercera característica más importante, con una importancia de aproximadamente 0.1020."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "f212ceec",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "f212ceec",
+        "outputId": "d6878405-8824-4367-9831-9aa0014f3036"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Árbol de Decisión: Accuracy = 0.61, Desviación estándar = 0.49\n",
+            "Random Forest: Accuracy = 0.69, Desviación estándar = 0.46\n",
+            "SVM: Accuracy = 0.56, Desviación estándar = 0.50\n",
+            "K-NN: Accuracy = 0.54, Desviación estándar = 0.50\n",
+            "El mejor modelo es: Random Forest\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Escalar características\n",
+        "scaler = StandardScaler()\n",
+        "X_train = scaler.fit_transform(X_train)\n",
+        "X_test = scaler.transform(X_test)\n",
+        "\n",
+        "# Modelos (excluyendo MultinomialNB)\n",
+        "models = [\n",
+        "    (\"Árbol de Decisión\", DecisionTreeClassifier()),\n",
+        "    (\"Random Forest\", RandomForestClassifier()),\n",
+        "    (\"SVM\", SVC()),\n",
+        "    (\"K-NN\", KNeighborsClassifier())\n",
+        "]\n",
+        "\n",
+        "# Entrenar y evaluar modelos\n",
+        "results = {}\n",
+        "\n",
+        "for model_name, model in models:\n",
+        "    model.fit(X_train, y_train)\n",
+        "    y_pred = model.predict(X_test)\n",
+        "    accuracy = accuracy_score(y_test, y_pred)\n",
+        "    results[model_name] = {\n",
+        "        'accuracy': accuracy,\n",
+        "        'std_dev': np.std(y_pred == y_test)  # Calcula la desviación estándar\n",
+        "    }\n",
+        "\n",
+        "# Imprimir resultados\n",
+        "for model_name, metrics in results.items():\n",
+        "    print(f\"{model_name}: Accuracy = {metrics['accuracy']:.2f}, Desviación estándar = {metrics['std_dev']:.2f}\")\n",
+        "\n",
+        "best_model_name = max(results, key=lambda k: results[k]['accuracy'])\n",
+        "print(f\"El mejor modelo es: {best_model_name}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f7072776",
+      "metadata": {
+        "id": "f7072776"
+      },
+      "source": [
+        "**Conclusión:**\n",
+        "\n",
+        "- **Modelo: Árbol de Decisión**\n",
+        "\n",
+        "  _Precisión_: con valores del 62% y una desviación estándar de 0.48%. La desviación estándar indica una variabilidad moderada, lo cual sugiere una aceptable consistencia en las predicciones.\n",
+        "\n",
+        "- **Modelo: Random Forest**\n",
+        "\n",
+        "  _Precisión_: Con valores del 69% y una desviación estándar de 0.46%. Este modelo ha demostrado una mayor precisión en la clasificación de la calidad del vino en comparación con el modelo anterior, sugiriendo una mayor consistencia en sus predicciones en comparación con los modelos anteriores.\n",
+        "\n",
+        "- **Modelo: SVM (Support Vector Machine)**\n",
+        "\n",
+        "  _Precisión_: con valores del 56% y una desviación estándar de 0.50%. Esto indica que el modelo acertó en su predicción de la calidad del vino en aproximadamente el 56% de los casos. Sin embargo, la desviación estándar de 0.50% sugiere que el rendimiento del modelo puede variar en alrededor de 0.50%, indicando una variabilidad significativa en las predicciones que podría afectar la consistencia del modelo en diferentes situaciones.\n",
+        "\n",
+        "- **Modelo: K-NN (K-Nearest Neighbors)**\n",
+        "\n",
+        "  _Precisión_: con valores del 54% y una desviación estándar de 0.50%. Donde el modelo acertó en su predicción de la calidad del vino en aproximadamente el 54% de los casos. Similar al modelo SVM, la desviación estándar de 0.50% sugiere que el rendimiento del modelo puede variar en alrededor de 0.50%. Esta variabilidad en las predicciones impacta la consistencia del modelo, ya que las predicciones pueden ser menos predecibles en diferentes circunstancias.\n",
+        "\n",
+        "En resumen, el **modelo Random Forest** sigue siendo el **más eficaz** en la clasificación de la calidad del vino, con la precisión más alta en comparación a alos otros modelos."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Ahora que sabemos que el mejor modelo es **Random Forest**, vamos a hacer hincapié y mejorar ciertos aspectos. Empezaremos por estandarizar los datos antes de entrenar el modelo para mejorar su la estabilidad, convergencia y rendimiento, garantizando que las características tengan una escala coherente. Estas mejoras son particularmente relevantes cuando se trabaja con algoritmos sensibles a las diferencias en las escalas de las características"
+      ],
+      "metadata": {
+        "id": "mGCMrPjRIRCr"
+      },
+      "id": "mGCMrPjRIRCr"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "ef23761d",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ef23761d",
+        "outputId": "7c9e52fa-1863-421b-9159-566bb5f037c7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Precisión en el conjunto de prueba: 0.69\n",
+            "Desviación estándar en el conjunto de prueba: 0.46\n",
+            "Informe de Clasificación:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           3       1.00      0.00      0.00         5\n",
+            "           4       0.56      0.20      0.29        25\n",
+            "           5       0.70      0.70      0.70       291\n",
+            "           6       0.66      0.79      0.72       432\n",
+            "           7       0.76      0.58      0.66       192\n",
+            "           8       0.80      0.46      0.58        35\n",
+            "\n",
+            "    accuracy                           0.69       980\n",
+            "   macro avg       0.75      0.45      0.49       980\n",
+            "weighted avg       0.70      0.69      0.68       980\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import accuracy_score, classification_report\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "import numpy as np\n",
+        "\n",
+        "# Crear una instancia de StandardScaler\n",
+        "scaler = StandardScaler()\n",
+        "\n",
+        "# Ajustar y transformar los datos de entrenamiento\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "\n",
+        "# Transformar los datos de prueba utilizando el mismo escalador\n",
+        "X_test_scaled = scaler.transform(X_test)\n",
+        "\n",
+        "# Random Forest\n",
+        "best_model = RandomForestClassifier(random_state=42)\n",
+        "\n",
+        "best_model.fit(X_train_scaled, y_train)\n",
+        "\n",
+        "# Predicciones\n",
+        "y_pred = best_model.predict(X_test_scaled)\n",
+        "\n",
+        "# Calcular la precisión\n",
+        "accuracy = accuracy_score(y_test, y_pred)\n",
+        "print(f\"Precisión en el conjunto de prueba: {accuracy:.2f}\")\n",
+        "\n",
+        "# Calcular la desviación estándar\n",
+        "std_dev = np.std(y_pred == y_test)\n",
+        "print(f\"Desviación estándar en el conjunto de prueba: {std_dev:.2f}\")\n",
+        "\n",
+        "# Generar el informe de clasificación\n",
+        "report = classification_report(y_test, y_pred, zero_division=1)\n",
+        "\n",
+        "print(\"Informe de Clasificación:\")\n",
+        "print(report)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5a5ee4da",
+      "metadata": {
+        "id": "5a5ee4da"
+      },
+      "source": [
+        "_**Conclusión**_: No vemos cambios en los valores que arrojo el modelo, pero vamos analizar las otras metricas:\n",
+        "\n",
+        "1. **Precisión en el conjunto de prueba: 0.69**\n",
+        "   - La precisión del modelo es del 69%, lo cual indica la proporción de instancias clasificadas correctamente.\n",
+        "\n",
+        "2. **Desviación estándar en el conjunto de prueba: 0.46**\n",
+        "   - La desviación estándar mide la dispersión de los valores. En este caso, la desviación estándar relativamente alta (0.46) en el conjunto de prueba puede sugerir cierta variabilidad en el rendimiento del modelo para diferentes conjuntos de datos de prueba.\n",
+        "\n",
+        "3. **Informe de Clasificación:**\n",
+        "\n",
+        "      - **Clase 3:**\n",
+        "         - La precisión es del 100%, pero el recall es muy bajo (0%), lo que indica que el modelo tiene dificultades para identificar correctamente las instancias de esta clase.\n",
+        "      - **Clase 4:**\n",
+        "         - La precisión es del 56%, lo que indica que el 56% de las predicciones positivas son correctas. El recall es del 20%, lo que sugiere que hay instancias de esta clase que el modelo no está capturando adecuadamente.\n",
+        "\n",
+        "      - **Clase 5:**\n",
+        "         - La precisión y el recall son razonablemente buenos (70%), indicando que el modelo tiene un buen rendimiento en esta clase.\n",
+        "\n",
+        "      - **Clase 6:**\n",
+        "         - Tanto la precisión como el recall son aceptables (66% y 79% respectivamente). El modelo tiende a clasificar bien las instancias de esta clase.\n",
+        "\n",
+        "      - **Clase 7:**\n",
+        "         - La precisión es del 76%, pero el recall es del 58%. El modelo tiene un rendimiento decente, pero podría mejorar en la identificación de todas las instancias positivas de esta clase.\n",
+        "\n",
+        "      - **Clase 8:**\n",
+        "         - La precisión es alta (80%), pero el recall es bajo (46%). El modelo tiende a ser preciso al predecir esta clase, pero deja de capturar algunas instancias.\n",
+        "\n",
+        "4. **Métricas globales:**\n",
+        "   - **Accuracy (exactitud):** 69%. Representa la proporción de instancias correctamente clasificadas en todas las clases.\n",
+        "   - **Macro AVG:** Representa la media no ponderada de precision, recall y f1-score para todas las clases. En este caso, es 0.75 para precision, 0.45 para recall, y 0.49 para f1-score.\n",
+        "   - **Weighted AVG:** Similar al macro AVG, pero ponderado por el número de instancias en cada clase. En este caso, la precisión ponderada es del 70%, recall ponderado es del 69%, y f1-score ponderado es del 68%.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "f2c263a1",
+      "metadata": {
+        "scrolled": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 565
+        },
+        "id": "f2c263a1",
+        "outputId": "b01f7959-756b-4e89-a561-09fc495d2811"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "\n",
+        "plt.scatter(y_test, y_test, alpha=0.5, color='blue', label='Valores Reales (y_test)', marker='o', s=50)\n",
+        "plt.scatter(y_test, y_pred, alpha=0.5, color='green', label='Predicciones (y_pred)', marker='^', s=50)\n",
+        "\n",
+        "plt.xlabel('Calidad del Vino')\n",
+        "plt.ylabel('Calidad del Vino')\n",
+        "plt.title('Comparación entre Valores Reales y Predicciones')\n",
+        "plt.grid(True)\n",
+        "plt.legend()\n",
+        "\n",
+        "plt.plot([2, 9], [2, 9], color='gray', linestyle='--', label='Igualdad Perfecta')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0b17b83e",
+      "metadata": {
+        "id": "0b17b83e"
+      },
+      "source": [
+        "Este gráfico que representa los valores reales (y_test) en azul (círculos) y las predicciones (y_pred) en verde (triángulos) proporciona una forma visual de comparar cómo se relacionan las predicciones del modelo con los valores reales.\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "fd13b57b",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "fd13b57b",
+        "outputId": "afcbff52-0e25-4a0b-af8c-a67f53457995"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "y_classes, class_counts = np.unique(y, return_counts=True)\n",
+        "\n",
+        "plt.bar(y_classes, class_counts)\n",
+        "plt.xlabel('Clases de Calidad del Vino')\n",
+        "plt.ylabel('Número de Muestras')\n",
+        "plt.title('Distribución de Clases en Wine Quality Dataset')\n",
+        "plt.xticks(y_classes)\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "id": "1577a5c4",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1577a5c4",
+        "outputId": "97755129-dba0-49e9-ecbc-8b76eaeb335b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Clase 3: 20 instancias\n",
+            "Clase 4: 163 instancias\n",
+            "Clase 5: 1457 instancias\n",
+            "Clase 6: 2198 instancias\n",
+            "Clase 7: 880 instancias\n",
+            "Clase 8: 175 instancias\n",
+            "Clase 9: 5 instancias\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_classes, class_counts = np.unique(y, return_counts=True)\n",
+        "\n",
+        "for y_class, count in zip(y_classes, class_counts):\n",
+        "    print(f'Clase {y_class}: {count} instancias')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1daefcf9",
+      "metadata": {
+        "id": "1daefcf9"
+      },
+      "source": [
+        "Al analizar el numero de instancias de la variable dependiente (Calidad del Vino) sobre algunas de las observaciones previas temos que:\n",
+        "\n",
+        "1. **Clase 3: 20 instancias**\n",
+        "   - Con solo 20 instancias, la precisión fue del 100% pero un recall del 0% tiene más sentido. Ya que falta de datos puede hacer que el modelo tenga dificultades para aprender patrones representativos de esta clase.\n",
+        "\n",
+        "2. **Clase 4: 163 instancias**\n",
+        "   - Con 163 instancias, la precisión del 56% y el recall del 20% indican que el modelo tiene dificultades para identificar correctamente la mayoría de las instancias de esta clase pero al tener más muestras va aumentando su valor.\n",
+        "\n",
+        "3. **Clase 5: 1457 instancias**\n",
+        "   - Con un número significativo de instancias (1457), la precisión y el recall del 70% sugieren un rendimiento razonable en esta clase.\n",
+        "\n",
+        "4. **Clase 6: 2198 instancias**\n",
+        "   - El modelo tiene un rendimiento aceptable en esta clase, con una precisión del 66% y un recall del 79%. Dado el número considerable de instancias, el modelo tiene un buen desempeño .\n",
+        "\n",
+        "5. **Clase 7: 880 instancias**\n",
+        "   - La precisión del 76% y el recall del 58% sugieren un rendimiento decente en esta clase, pero hay margen para mejorar, especialmente en el recall.\n",
+        "\n",
+        "6. **Clase 8: 175 instancias**\n",
+        "   - La alta precisión del 80% y el recall bajo del 46% indican que el modelo tiende a ser preciso al predecir esta clase, pero no logra identificar todas las instancias.\n",
+        "\n",
+        "La interpretación anterior se mantiene, pero ahora con el contexto del desequilibrio de clase, entendemos que las clases con un número menor de instancias pueden presentar desafíos para el modelo, y podrían ser necesarias estrategias como el aumento de datos, el submuestreo o el ajuste de pesos de clase para abordar este problema.\n",
+        "\n",
+        "Además, al considerar la métrica macro AVG y weighted AVG, estas métricas también reflejan el impacto del desequilibrio de clases en el rendimiento general del modelo. La precisión ponderada (weighted AVG) se ve afectada por el desequilibrio de clases, ya que las clases más grandes tienen un impacto mayor en esta métrica."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3cf23a60",
+      "metadata": {
+        "id": "3cf23a60"
+      },
+      "source": [
+        "### **_Conclusiones preliminares_** :\n",
+        "\n",
+        "1. **Clases Mayoritarias:** Debido al desequilibrio de clases, el modelo tiene una cantidad mucho mayor de ejemplos de las clases \"5\" y \"6\" en comparación con otras clases. Esto hace que el modelo tenga más información sobre estas clases, por lo que puede ser más preciso al predecirlas. Esto se refleja en los altos valores de precisión y recall para las clases \"5\" y \"6\".\n",
+        "\n",
+        "2. **Clases Minoritarias:** Por otro lado, las clases minoritarias, como \"3\", \"4\", \"7\", \"8\" y \"9\", tienen menos ejemplos en el conjunto de datos. Esto hace que el modelo tenga menos información para aprender y predecir estas clases, lo que se traduce en valores más bajos de precisión y recall para estas clases.\n",
+        "\n",
+        "3. **Impacto en el Promedio Ponderado:** El promedio ponderado de todas las métricas se ve influenciado por el desequilibrio de clases. Dado que las clases \"5\" y \"6\" tienen muchas más muestras que las clases minoritarias, su impacto es mayor en el promedio ponderado. Esto significa que el rendimiento del modelo en las clases minoritarias puede no reflejarse tan claramente en el promedio ponderado.\n",
+        "\n",
+        "4. **Contexto del Problema:** En algunos casos, el desequilibrio de clases puede ser natural en un problema de clasificación. Por ejemplo, en el caso del vino, es más común encontrar vinos de calidad \"5\" y \"6\" que de calidad \"3\" o \"9\".\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c6ae325b",
+      "metadata": {
+        "id": "c6ae325b"
+      },
+      "source": [
+        "# **Estrategias para mejora el modelo:**\n",
+        "\n",
+        "- Dado el desequilibrio de clases, podrías considerar estrategias de manejo de desequilibrio, como oversampling o undersampling de clases minoritarias, para mejorar el rendimiento del modelo en las clases desequilibradas.\n",
+        "- La ingeniería de características y la búsqueda de hiperparámetros podrían ayudar a mejorar aún más el rendimiento del modelo.\n",
+        "- Podrías evaluar otros modelos de clasificación y ajustar sus hiperparámetros para determinar si hay un modelo más adecuado para este conjunto de datos específico.\n",
+        "- Un análisis de residuos podría ayudarte a entender mejor las predicciones erróneas del modelo y cómo se pueden abordar.\n",
+        "\n",
+        "La estrategia más útil sería el oversampling de las clases minoritarias. Dado que las clases \"3\", \"4\", \"7\", \"8\" y \"9\" tienen menos instancias que las clases mayoritarias, aumentar el número de muestras en estas clases puede ayudar al modelo a aprender de manera más equitativa y mejorar su rendimiento en las clases desequilibradas."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "1j3ow3IMtfbv"
+      },
+      "id": "1j3ow3IMtfbv"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "id": "4b8a0466",
+      "metadata": {
+        "scrolled": true,
+        "id": "4b8a0466"
+      },
+      "outputs": [],
+      "source": [
+        "from imblearn.over_sampling import RandomOverSampler\n",
+        "\n",
+        "# instancia del resampler\n",
+        "oversampler = RandomOverSampler(random_state=42)\n",
+        "\n",
+        "# Aplica el resampling\n",
+        "X_train_resampled, y_train_resampled = oversampler.fit_resample(X_train, y_train)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "id": "8dbd38e7",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8dbd38e7",
+        "outputId": "8e3852de-07a3-4bf5-bae3-e8b89698d310"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Precisión en el conjunto de prueba: 0.92\n",
+            "Informe de Clasificación:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           3       1.00      1.00      1.00       363\n",
+            "           4       0.99      1.00      1.00       338\n",
+            "           5       0.78      0.88      0.83       346\n",
+            "           6       0.81      0.66      0.73       375\n",
+            "           7       0.89      0.94      0.91       383\n",
+            "           8       0.99      1.00      1.00       332\n",
+            "           9       0.99      1.00      1.00       336\n",
+            "\n",
+            "    accuracy                           0.92      2473\n",
+            "   macro avg       0.92      0.93      0.92      2473\n",
+            "weighted avg       0.92      0.92      0.92      2473\n",
+            "\n",
+            "Desviación estándar de las predicciones probabilísticas: 0.32\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "# datos en entrenamiento y prueba\n",
+        "X_train_balanced, X_test, y_train_balanced, y_test = train_test_split(X_train_resampled, y_train_resampled, test_size=0.2, random_state=42)\n",
+        "\n",
+        "# modelo RandomForest\n",
+        "model = RandomForestClassifier(random_state=42)\n",
+        "model.fit(X_train_balanced, y_train_balanced)\n",
+        "\n",
+        "\n",
+        "y_pred_balanced = model.predict(X_test)\n",
+        "\n",
+        "# predicciones probabilísticas\n",
+        "y_pred_prob = model.predict_proba(X_test)\n",
+        "\n",
+        "# desviación estándar\n",
+        "std_deviation = y_pred_prob.std()\n",
+        "\n",
+        "# precisión en el conjunto de prueba\n",
+        "accuracy = accuracy_score(y_test, y_pred_balanced)\n",
+        "print(f'Precisión en el conjunto de prueba: {accuracy:.2f}')\n",
+        "\n",
+        "# Informe de clasificación\n",
+        "report = classification_report(y_test, y_pred_balanced)\n",
+        "print('Informe de Clasificación:')\n",
+        "print(report)\n",
+        "\n",
+        "# desviación estándar\n",
+        "print(f'Desviación estándar de las predicciones probabilísticas: {std_deviation:.2f}')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "id": "f72c45f7",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 565
+        },
+        "id": "f72c45f7",
+        "outputId": "054aec79-0e91-4e4d-d4e1-9384ab09bea2"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(8, 6))\n",
+        "\n",
+        "plt.scatter(y_test, y_test, alpha=0.5, color='blue', label='Valores Reales (y_test)', marker='o', s=50)\n",
+        "plt.scatter(y_test, y_pred_balanced, alpha=0.5, color='green', label='Predicciones (y_pred)', marker='^', s=50)\n",
+        "\n",
+        "plt.xlabel('Calidad del Vino')\n",
+        "plt.ylabel('Calidad del Vino')\n",
+        "plt.title('Comparación entre Valores Reales y Predicciones')\n",
+        "plt.grid(True)\n",
+        "plt.legend()\n",
+        "\n",
+        "plt.plot([2, 9], [2, 9], color='gray', linestyle='--', label='Igualdad Perfecta')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cfc1528e",
+      "metadata": {
+        "id": "cfc1528e"
+      },
+      "source": [
+        "## **Discución**\n",
+        "\n",
+        "En esta discusión, se buscó simular los desafíos que pueden surgir al trabajar con un conjunto de datos y se exploraron diversos mecanismos para mejorar su rendimiento. Se llevó la aplicación de varios modelos de clasificación, como Árboles de Decisión, Random Forest, SVM y K-NN, sobre el conjunto de datos de calidad de vino blanco de Wine Quality UCI.\n",
+        "\n",
+        "Los resultados revelaron que el desbalance de clases en el conjunto de datos tuvo un impacto significativo en la precisión de los modelos, siendo las clases mayoritarias las que presentaron un mejor rendimiento. Para hacer frente a este desbalance, se implementó la estrategia de oversampling, la cual condujo a una mejora sustancial en la precisión del modelo Random Forest, elevándola de 0.69 a 0.92 en el conjunto de prueba y de pasar a una desvación estandar de 0.46 a 0.32.\n",
+        "\n",
+        "La elección de estos modelos específicos se basó en su capacidad para manejar datos multidimensionales y su disponibilidad en bibliotecas de aprendizaje automático ampliamente utilizadas. Es importante destacar que las diferencias metodológicas entre el enfoque propuesto y el estudio de referencia se centran en las estrategias de preprocesamiento y la evaluación de modelos. Aunque ambos estudios emplearon algoritmos similares, las estrategias metodológicas y los resultados variaron debido a las adaptaciones realizadas según las necesidades específicas de cada investigación.\n",
+        "\n",
+        "Este estudio subraya la importancia de abordar el desbalance de clases en conjuntos de datos y de ajustar la metodología según las características particulares del problema de clasificación. La adaptación de estrategias de preprocesamiento y evaluación es esencial para lograr resultados más precisos y relevantes en el ámbito de la clasificación de datos.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.16"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/_config.yml b/_config.yml
index fb6e996934..651c5e6a82 100644
--- a/_config.yml
+++ b/_config.yml
@@ -1,14 +1,14 @@
-title:              Clean Blog
-email:              your-email@example.com
-description:        A Blog Theme by Start Bootstrap
-author:             Start Bootstrap
-baseurl:            "/startbootstrap-clean-blog-jekyll"
-url:                "https://startbootstrap.github.io"
+title:              Data Lab
+email:              diazaguirrejohanna@gmail.com
+description:        "Analytics and customized solutions for your business or project. <br> by Johanna Diaz Aguirre "
+author:             "Start Bootstrap"
+baseurl:            ""
+url:                "https://Cjohanna.github.io"
 
 # Social Profiles
-twitter_username:   SBootstrap
-github_username:    StartBootstrap
-facebook_username:  StartBootstrap
+twitter_username:   
+github_username:    
+facebook_username:  
 instagram_username:  
 linkedin_username:
 
diff --git a/_includes/head.html b/_includes/head.html
index 7848045415..2f0637515b 100644
--- a/_includes/head.html
+++ b/_includes/head.html
@@ -12,6 +12,7 @@
 
   <link href='https://fonts.googleapis.com/css?family=Lora:400,700,400italic,700italic' rel='stylesheet' type='text/css'>
   <link href='https://fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,600italic,700italic,800italic,400,300,600,700,800' rel='stylesheet' type='text/css'>
+  
 
   <script src="https://use.fontawesome.com/releases/v5.15.3/js/all.js" crossorigin="anonymous"></script>
 
diff --git a/_includes/navbar.html b/_includes/navbar.html
index c514748a08..75d7dc7e77 100644
--- a/_includes/navbar.html
+++ b/_includes/navbar.html
@@ -15,7 +15,7 @@
           <a class="nav-link" href="{{"/about" | relative_url }}">About</a>
         </li>
         <li class="nav-item">
-          <a class="nav-link" href="{{ "/posts" | relative_url }}">Posts</a>
+          <a class="nav-link" href="{{ "/posts" | relative_url }}">Projects</a>
         </li>
         <li class="nav-item">
           <a class="nav-link" href="{{"/contact" | relative_url }}">Contact</a>
diff --git a/_layouts/home.html b/_layouts/home.html
index 6501456567..daec7dc571 100644
--- a/_layouts/home.html
+++ b/_layouts/home.html
@@ -41,15 +41,6 @@ <h3 class="post-subtitle">{{ post.subtitle }}</h3>
             <h3 class="post-subtitle">{{ post.excerpt | strip_html | truncatewords: 15 }}</h3>
             {% endif %}
           </a>
-          <p class="post-meta">Posted by
-            {% if post.author %}
-            {{ post.author }}
-            {% else %}
-            {{ site.author }}
-            {% endif %}
-            on
-            {{ post.date | date: '%B %d, %Y' }} &middot; {% include read_time.html content=post.content %}            
-          </p>
         </article>
 
         <hr>
diff --git a/_layouts/post.html b/_layouts/post.html
index d4bf8fa7d8..356b681c47 100644
--- a/_layouts/post.html
+++ b/_layouts/post.html
@@ -17,10 +17,7 @@ <h1>{{ page.title }}</h1>
             {% if page.subtitle %}
             <h2 class="subheading">{{ page.subtitle }}</h2>
             {% endif %}
-            <span class="meta">Posted by
-              <a href="#">{% if page.author %}{{ page.author }}{% else %}{{ site.author }}{% endif %}</a>
-              on {{ page.date | date: '%B %d, %Y' }} &middot; {% include read_time.html
-              content=page.content %}
+            <span class="meta">Posted by Johanna Diaz
             </span>
           </div>
         </div>
diff --git a/_posts/.html b/_posts/.html
new file mode 100644
index 0000000000..258dc4a308
--- /dev/null
+++ b/_posts/.html
@@ -0,0 +1,62 @@
+---
+layout: post
+title: "Wine Quality Predictor"
+subtitle: "Exploring wine quality through analysis and prediction"
+date: 2023-06-28 23:45:13 -0400
+background: '/img/posts/03.jpg'
+---
+
+<p>This project aims to apply Machine Learning techniques to develop predictive models that can predict the quality of wine based on its physicochemical attributes. To achieve this, visualization and exploratory analysis techniques will be explored to understand the distribution of the attributes and their impact on prediction. Additionally, the crucial role of data preprocessing and transformation in the modeling phase will be highlighted.</p>
+
+<h2 class="section-heading">Description of Dataset</h2>
+
+<p><strong>Source:</strong> This dataset was created by P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis. The "Wine Quality" dataset is available in the UCI Machine Learning Repository.</p>
+
+<p>The "Wine Quality" dataset is commonly used in classification and regression problems to predict wine quality. It was collected to evaluate the quality of white and red wines based on physicochemical attributes. The inputs were gathered through physicochemical tests, and the output is based on sensory data obtained from evaluations conducted by wine experts. Each expert rated the wine quality on a scale from 0 (very poor) to 10 (excellent).</p>
+
+<p> <strong> Attribute Description:</strong>The "Wine Quality" dataset consists of instances of white and red wines, with a total of 11 numerical attributes. These attributes include characteristics such as acidity, sugar levels, sulfates, and alcohol content. The wine quality is represented by a categorical target variable. </p>
+
+<blockquote class="blockquote">Input Attributes (x):</blockquote>
+<ul>
+    <li>Fixed acidity (Acidez fija): g/dm³ (float)</li>
+    <li>Volatile acidity (Acidez volátil): g/dm³ (float)</li>
+    <li>Citric acid (Ácido cítrico): g/dm³ (float)</li>
+    <li>Residual sugar (Azúcar residual): g/dm³ (float)</li>
+    <li>Chlorides (Cloruros): g/dm³ (float)</li>
+    <li>Free sulfur dioxide (Dióxido de azufre libre): mg/dm³ (float)</li>
+    <li>Total sulfur dioxide (Dióxido de azufre total): mg/dm³ (float)</li>
+    <li>Density (Densidad): g/cm³ (float)</li>
+    <li>pH: (float)</li>
+    <li>Sulphates (Sulfatos): g/dm³ (float)</li>
+    <li>Alcohol (Alcohol): % vol (float)</li>
+</ul>
+  
+<blockquote class="blockquote">Output Attributes(y):</blockquote>
+<ul> 
+    <li>Wine Quality: It is evaluated on a discrete scale of 0 to 10, where a higher value indicates better quality</li>
+</ul>
+<h2 class="section-heading">Exploratory Data Analysis</h2>
+<img class="img-fluid" src="https://images.unsplash.com/photo-1542903660-eedba2cda473?auto=format&fit=crop&q=80&w=2070&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" alt="Demo Image">
+<P>Before diving into the predictive modeling, an initial step is to perform an Exploratory Data Analysis (EDA). For this purpose, you can load the dataset into your working environment, such as Python with Pandas, and execute the following tasks:</P>
+
+        <p>This repository contains the results of a comprehensive analysis of a white wine dataset provided by the reference researchers. The analysis involved three key stages: data preprocessing, modeling and evaluation, and performance improvement strategies.</p>
+    
+        <p> <strong> Data Preprocessing</strong></p>
+        <p>In the data preprocessing phase, we applied essential techniques, such as feature scaling using StandardScaler. This ensured that all features contributed equally to the modeling process, thus avoiding the dominance of certain attributes over others.</p>
+    
+        <p> <strong> Modeling</strong></p>
+        <p>In the modeling stage, we deliberately selected four specific algorithms: Decision Tree, Random Forest, SVM, and K-NN. The choice of these algorithms was based on their demonstrated ability to address classification problems in complex datasets. It is important to note that, although some algorithms were shared with the reference study, the methodologies differed in aspects such as hyperparameter tuning and how class imbalance was addressed.</p>
+        
+        <p> <strong> Evaluation</strong></p>
+        <p>The reference study provided results that served as a starting point for model evaluation. It was observed that, despite achieving acceptable levels of accuracy, class imbalance affected the model's ability to generalize to minority classes. This finding was crucial in determining the need to implement an oversampling strategy.</p>
+    
+        <p> <strong> Performance Improvement</strong></p>
+        <p>The oversampling strategy was the key element in significantly improving the model's performance. By increasing the number of instances in the minority classes, the class distribution was balanced, allowing the model to learn more effectively from all classes. This resulted in a noticeable increase in accuracy on the test set, reaching an impressive 92%.</p>
+        
+        <p> <strong> Conclusion</strong></p>
+        <p>While both methodologies shared the use of some common algorithms, they differed in their approach to addressing class imbalance and the specific model configurations. This distinction is fundamental and highlights the importance of adapting methodologies to the unique characteristics of each dataset and problem.</p>
+    
+        <p>For more details, refer to the complete analysis and code in this repository.</p> 
+        <a href="https://colab.research.google.com/drive/1CpCsCjCLkUBLazwJL5NQSS70D6kBgIbr?authuser=1" target="_blank" class="btn btn-primary">Open in Google Colab</a>
+      
+<p>Photographs by <a href="https://unsplash.com/">Unsplash</a>.</p>
diff --git a/_posts/2020-01-26-dinosaurs.html b/_posts/2020-01-26-dinosaurs.html
deleted file mode 100644
index fc3c9f0b6a..0000000000
--- a/_posts/2020-01-26-dinosaurs.html
+++ /dev/null
@@ -1,40 +0,0 @@
----
-layout: post
-title: "Dinosaurs are extinct today"
-subtitle: "because they lacked opposable thumbs and the brainpower to build a space program."
-date: 2020-01-26 23:45:13 -0400
-background: '/img/posts/01.jpg'
----
-
-<p>Never in all their history have men been able truly to conceive of the world as one: a single sphere, a globe, having the qualities of a globe, a round earth in which all the directions eventually meet, in which there is no center because every point, or none, is center — an equal earth which all men occupy as equals. The airman's earth, if free men make it, will be truly round: a globe in practice, not in theory.</p>
-
-<p>Science cuts two ways, of course; its products can be used for both good and evil. But there's no turning back from science. The early warnings about technological dangers also come from science.</p>
-
-<p>What was most significant about the lunar voyage was not that man set foot on the Moon but that they set eye on the earth.</p>
-
-<p>A Chinese tale tells of some men sent to harm a young girl who, upon seeing her beauty, become her protectors rather than her violators. That's how I felt seeing the Earth for the first time. I could not help but love and cherish her.</p>
-
-<p>For those who have seen the Earth from space, and for the hundreds and perhaps thousands more who will, the experience most certainly changes your perspective. The things that we share in our world are far more valuable than those which divide us.</p>
-
-<h2 class="section-heading">The Final Frontier</h2>
-
-<p>There can be no thought of finishing for ‘aiming for the stars.’ Both figuratively and literally, it is a task to occupy the generations. And no matter how much progress one makes, there is always the thrill of just beginning.</p>
-
-<p>There can be no thought of finishing for ‘aiming for the stars.’ Both figuratively and literally, it is a task to occupy the generations. And no matter how much progress one makes, there is always the thrill of just beginning.</p>
-
-<blockquote class="blockquote">The dreams of yesterday are the hopes of today and the reality of tomorrow. Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next ten.</blockquote>
-
-<p>Spaceflights cannot be stopped. This is not the work of any one man or even a group of men. It is a historical process which mankind is carrying out in accordance with the natural laws of human development.</p>
-
-<h2 class="section-heading">Reaching for the Stars</h2>
-
-<p>As we got further and further away, it [the Earth] diminished in size. Finally it shrank to the size of a marble, the most beautiful you can imagine. That beautiful, warm, living object looked so fragile, so delicate, that if you touched it with a finger it would crumble and fall apart. Seeing this has to change a man.</p>
-
-<img class="img-fluid" src="https://source.unsplash.com/Mn9Fa_wQH-M/800x450" alt="Demo Image">
-<span class="caption text-muted">To go places and do things that have never been done before – that’s what living is all about.</span>
-
-<p>Space, the final frontier. These are the voyages of the Starship Enterprise. Its five-year mission: to explore strange new worlds, to seek out new life and new civilizations, to boldly go where no man has gone before.</p>
-
-<p>As I stand out here in the wonders of the unknown at Hadley, I sort of realize there’s a fundamental truth to our nature, Man must explore, and this is exploration at its greatest.</p>
-
-<p>Placeholder text by <a href="http://spaceipsum.com/">Space Ipsum</a>. Photographs by <a href="https://unsplash.com/">Unsplash</a>.</p>
diff --git a/_posts/2020-01-27-dreams.html b/_posts/2020-01-27-dreams.html
deleted file mode 100644
index f687623ea2..0000000000
--- a/_posts/2020-01-27-dreams.html
+++ /dev/null
@@ -1,39 +0,0 @@
----
-layout: post
-title: "The dreams of yesterday are the hopes of today and the reality of tomorrow."
-date: 2020-01-27 23:45:13 -0400
-background: '/img/posts/02.jpg'
----
-
-<p>Never in all their history have men been able truly to conceive of the world as one: a single sphere, a globe, having the qualities of a globe, a round earth in which all the directions eventually meet, in which there is no center because every point, or none, is center — an equal earth which all men occupy as equals. The airman's earth, if free men make it, will be truly round: a globe in practice, not in theory.</p>
-
-<p>Science cuts two ways, of course; its products can be used for both good and evil. But there's no turning back from science. The early warnings about technological dangers also come from science.</p>
-
-<p>What was most significant about the lunar voyage was not that man set foot on the Moon but that they set eye on the earth.</p>
-
-<p>A Chinese tale tells of some men sent to harm a young girl who, upon seeing her beauty, become her protectors rather than her violators. That's how I felt seeing the Earth for the first time. I could not help but love and cherish her.</p>
-
-<p>For those who have seen the Earth from space, and for the hundreds and perhaps thousands more who will, the experience most certainly changes your perspective. The things that we share in our world are far more valuable than those which divide us.</p>
-
-<h2 class="section-heading">The Final Frontier</h2>
-
-<p>There can be no thought of finishing for ‘aiming for the stars.’ Both figuratively and literally, it is a task to occupy the generations. And no matter how much progress one makes, there is always the thrill of just beginning.</p>
-
-<p>There can be no thought of finishing for ‘aiming for the stars.’ Both figuratively and literally, it is a task to occupy the generations. And no matter how much progress one makes, there is always the thrill of just beginning.</p>
-
-<blockquote class="blockquote">The dreams of yesterday are the hopes of today and the reality of tomorrow. Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next ten.</blockquote>
-
-<p>Spaceflights cannot be stopped. This is not the work of any one man or even a group of men. It is a historical process which mankind is carrying out in accordance with the natural laws of human development.</p>
-
-<h2 class="section-heading">Reaching for the Stars</h2>
-
-<p>As we got further and further away, it [the Earth] diminished in size. Finally it shrank to the size of a marble, the most beautiful you can imagine. That beautiful, warm, living object looked so fragile, so delicate, that if you touched it with a finger it would crumble and fall apart. Seeing this has to change a man.</p>
-
-<img class="img-fluid" src="https://source.unsplash.com/Mn9Fa_wQH-M/800x450" alt="Demo Image">
-<span class="caption text-muted">To go places and do things that have never been done before – that’s what living is all about.</span>
-
-<p>Space, the final frontier. These are the voyages of the Starship Enterprise. Its five-year mission: to explore strange new worlds, to seek out new life and new civilizations, to boldly go where no man has gone before.</p>
-
-<p>As I stand out here in the wonders of the unknown at Hadley, I sort of realize there’s a fundamental truth to our nature, Man must explore, and this is exploration at its greatest.</p>
-
-<p>Placeholder text by <a href="http://spaceipsum.com/">Space Ipsum</a>. Photographs by <a href="https://unsplash.com/">Unsplash</a>.</p>
diff --git a/_posts/2020-01-28-exploration.html b/_posts/2020-01-28-exploration.html
deleted file mode 100644
index ec94119725..0000000000
--- a/_posts/2020-01-28-exploration.html
+++ /dev/null
@@ -1,40 +0,0 @@
----
-layout: post
-title: "Failure is not an option"
-subtitle: "Many say exploration is part of our destiny, but it’s actually our duty to future generations."
-date: 2020-01-28 23:45:13 -0400
-background: '/img/posts/03.jpg'
----
-
-<p>Never in all their history have men been able truly to conceive of the world as one: a single sphere, a globe, having the qualities of a globe, a round earth in which all the directions eventually meet, in which there is no center because every point, or none, is center — an equal earth which all men occupy as equals. The airman's earth, if free men make it, will be truly round: a globe in practice, not in theory.</p>
-
-<p>Science cuts two ways, of course; its products can be used for both good and evil. But there's no turning back from science. The early warnings about technological dangers also come from science.</p>
-
-<p>What was most significant about the lunar voyage was not that man set foot on the Moon but that they set eye on the earth.</p>
-
-<p>A Chinese tale tells of some men sent to harm a young girl who, upon seeing her beauty, become her protectors rather than her violators. That's how I felt seeing the Earth for the first time. I could not help but love and cherish her.</p>
-
-<p>For those who have seen the Earth from space, and for the hundreds and perhaps thousands more who will, the experience most certainly changes your perspective. The things that we share in our world are far more valuable than those which divide us.</p>
-
-<h2 class="section-heading">The Final Frontier</h2>
-
-<p>There can be no thought of finishing for ‘aiming for the stars.’ Both figuratively and literally, it is a task to occupy the generations. And no matter how much progress one makes, there is always the thrill of just beginning.</p>
-
-<p>There can be no thought of finishing for ‘aiming for the stars.’ Both figuratively and literally, it is a task to occupy the generations. And no matter how much progress one makes, there is always the thrill of just beginning.</p>
-
-<blockquote class="blockquote">The dreams of yesterday are the hopes of today and the reality of tomorrow. Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next ten.</blockquote>
-
-<p>Spaceflights cannot be stopped. This is not the work of any one man or even a group of men. It is a historical process which mankind is carrying out in accordance with the natural laws of human development.</p>
-
-<h2 class="section-heading">Reaching for the Stars</h2>
-
-<p>As we got further and further away, it [the Earth] diminished in size. Finally it shrank to the size of a marble, the most beautiful you can imagine. That beautiful, warm, living object looked so fragile, so delicate, that if you touched it with a finger it would crumble and fall apart. Seeing this has to change a man.</p>
-
-<img class="img-fluid" src="https://source.unsplash.com/Mn9Fa_wQH-M/800x450" alt="Demo Image">
-<span class="caption text-muted">To go places and do things that have never been done before – that’s what living is all about.</span>
-
-<p>Space, the final frontier. These are the voyages of the Starship Enterprise. Its five-year mission: to explore strange new worlds, to seek out new life and new civilizations, to boldly go where no man has gone before.</p>
-
-<p>As I stand out here in the wonders of the unknown at Hadley, I sort of realize there’s a fundamental truth to our nature, Man must explore, and this is exploration at its greatest.</p>
-
-<p>Placeholder text by <a href="http://spaceipsum.com/">Space Ipsum</a>. Photographs by <a href="https://unsplash.com/">Unsplash</a>.</p>
diff --git a/_posts/2020-01-29-prophecy.html b/_posts/2020-01-29-prophecy.html
deleted file mode 100644
index 65704ea439..0000000000
--- a/_posts/2020-01-29-prophecy.html
+++ /dev/null
@@ -1,40 +0,0 @@
----
-layout: post
-title: "Science has not yet mastered prophecy"
-subtitle: "We predict too much for the next year and yet far too little for the next ten."
-date: 2020-01-29 23:45:13 -0400
-background: '/img/posts/04.jpg'
----
-
-<p>Never in all their history have men been able truly to conceive of the world as one: a single sphere, a globe, having the qualities of a globe, a round earth in which all the directions eventually meet, in which there is no center because every point, or none, is center — an equal earth which all men occupy as equals. The airman's earth, if free men make it, will be truly round: a globe in practice, not in theory.</p>
-
-<p>Science cuts two ways, of course; its products can be used for both good and evil. But there's no turning back from science. The early warnings about technological dangers also come from science.</p>
-
-<p>What was most significant about the lunar voyage was not that man set foot on the Moon but that they set eye on the earth.</p>
-
-<p>A Chinese tale tells of some men sent to harm a young girl who, upon seeing her beauty, become her protectors rather than her violators. That's how I felt seeing the Earth for the first time. I could not help but love and cherish her.</p>
-
-<p>For those who have seen the Earth from space, and for the hundreds and perhaps thousands more who will, the experience most certainly changes your perspective. The things that we share in our world are far more valuable than those which divide us.</p>
-
-<h2 class="section-heading">The Final Frontier</h2>
-
-<p>There can be no thought of finishing for ‘aiming for the stars.’ Both figuratively and literally, it is a task to occupy the generations. And no matter how much progress one makes, there is always the thrill of just beginning.</p>
-
-<p>There can be no thought of finishing for ‘aiming for the stars.’ Both figuratively and literally, it is a task to occupy the generations. And no matter how much progress one makes, there is always the thrill of just beginning.</p>
-
-<blockquote class="blockquote">The dreams of yesterday are the hopes of today and the reality of tomorrow. Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next ten.</blockquote>
-
-<p>Spaceflights cannot be stopped. This is not the work of any one man or even a group of men. It is a historical process which mankind is carrying out in accordance with the natural laws of human development.</p>
-
-<h2 class="section-heading">Reaching for the Stars</h2>
-
-<p>As we got further and further away, it [the Earth] diminished in size. Finally it shrank to the size of a marble, the most beautiful you can imagine. That beautiful, warm, living object looked so fragile, so delicate, that if you touched it with a finger it would crumble and fall apart. Seeing this has to change a man.</p>
-
-<img class="img-fluid" src="https://source.unsplash.com/Mn9Fa_wQH-M/800x450" alt="Demo Image">
-<span class="caption text-muted">To go places and do things that have never been done before – that’s what living is all about.</span>
-
-<p>Space, the final frontier. These are the voyages of the Starship Enterprise. Its five-year mission: to explore strange new worlds, to seek out new life and new civilizations, to boldly go where no man has gone before.</p>
-
-<p>As I stand out here in the wonders of the unknown at Hadley, I sort of realize there’s a fundamental truth to our nature, Man must explore, and this is exploration at its greatest.</p>
-
-<p>Placeholder text by <a href="http://spaceipsum.com/">Space Ipsum</a>. Photographs by <a href="https://unsplash.com/">Unsplash</a>.</p>
diff --git a/_posts/2020-01-30-heartbeats.html b/_posts/2020-01-30-heartbeats.html
deleted file mode 100644
index a444e6cd18..0000000000
--- a/_posts/2020-01-30-heartbeats.html
+++ /dev/null
@@ -1,39 +0,0 @@
----
-layout: post
-title: "I believe every human has a finite number of heartbeats. I don't intend to waste any of mine."
-date: 2020-01-30 23:45:13 -0400
-background: '/img/posts/05.jpg'
----
-
-<p>Never in all their history have men been able truly to conceive of the world as one: a single sphere, a globe, having the qualities of a globe, a round earth in which all the directions eventually meet, in which there is no center because every point, or none, is center — an equal earth which all men occupy as equals. The airman's earth, if free men make it, will be truly round: a globe in practice, not in theory.</p>
-
-<p>Science cuts two ways, of course; its products can be used for both good and evil. But there's no turning back from science. The early warnings about technological dangers also come from science.</p>
-
-<p>What was most significant about the lunar voyage was not that man set foot on the Moon but that they set eye on the earth.</p>
-
-<p>A Chinese tale tells of some men sent to harm a young girl who, upon seeing her beauty, become her protectors rather than her violators. That's how I felt seeing the Earth for the first time. I could not help but love and cherish her.</p>
-
-<p>For those who have seen the Earth from space, and for the hundreds and perhaps thousands more who will, the experience most certainly changes your perspective. The things that we share in our world are far more valuable than those which divide us.</p>
-
-<h2 class="section-heading">The Final Frontier</h2>
-
-<p>There can be no thought of finishing for ‘aiming for the stars.’ Both figuratively and literally, it is a task to occupy the generations. And no matter how much progress one makes, there is always the thrill of just beginning.</p>
-
-<p>There can be no thought of finishing for ‘aiming for the stars.’ Both figuratively and literally, it is a task to occupy the generations. And no matter how much progress one makes, there is always the thrill of just beginning.</p>
-
-<blockquote class="blockquote">The dreams of yesterday are the hopes of today and the reality of tomorrow. Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next ten.</blockquote>
-
-<p>Spaceflights cannot be stopped. This is not the work of any one man or even a group of men. It is a historical process which mankind is carrying out in accordance with the natural laws of human development.</p>
-
-<h2 class="section-heading">Reaching for the Stars</h2>
-
-<p>As we got further and further away, it [the Earth] diminished in size. Finally it shrank to the size of a marble, the most beautiful you can imagine. That beautiful, warm, living object looked so fragile, so delicate, that if you touched it with a finger it would crumble and fall apart. Seeing this has to change a man.</p>
-
-<img class="img-fluid" src="https://source.unsplash.com/Mn9Fa_wQH-M/800x450" alt="Demo Image">
-<span class="caption text-muted">To go places and do things that have never been done before – that’s what living is all about.</span>
-
-<p>Space, the final frontier. These are the voyages of the Starship Enterprise. Its five-year mission: to explore strange new worlds, to seek out new life and new civilizations, to boldly go where no man has gone before.</p>
-
-<p>As I stand out here in the wonders of the unknown at Hadley, I sort of realize there’s a fundamental truth to our nature, Man must explore, and this is exploration at its greatest.</p>
-
-<p>Placeholder text by <a href="http://spaceipsum.com/">Space Ipsum</a>. Photographs by <a href="https://unsplash.com/">Unsplash</a>.</p>
diff --git a/_posts/2020-01-31-man-must-explore.html b/_posts/2020-01-31-man-must-explore.html
deleted file mode 100644
index 4dd948b4d7..0000000000
--- a/_posts/2020-01-31-man-must-explore.html
+++ /dev/null
@@ -1,40 +0,0 @@
----
-layout: post
-title: "Man must explore, and this is exploration at its greatest"
-subtitle: "Problems look mighty small from 150 miles up"
-date: 2020-01-31 10:45:13 -0400
-background: '/img/posts/06.jpg'
----
-
-<p>Never in all their history have men been able truly to conceive of the world as one: a single sphere, a globe, having the qualities of a globe, a round earth in which all the directions eventually meet, in which there is no center because every point, or none, is center — an equal earth which all men occupy as equals. The airman's earth, if free men make it, will be truly round: a globe in practice, not in theory.</p>
-
-<p>Science cuts two ways, of course; its products can be used for both good and evil. But there's no turning back from science. The early warnings about technological dangers also come from science.</p>
-
-<p>What was most significant about the lunar voyage was not that man set foot on the Moon but that they set eye on the earth.</p>
-
-<p>A Chinese tale tells of some men sent to harm a young girl who, upon seeing her beauty, become her protectors rather than her violators. That's how I felt seeing the Earth for the first time. I could not help but love and cherish her.</p>
-
-<p>For those who have seen the Earth from space, and for the hundreds and perhaps thousands more who will, the experience most certainly changes your perspective. The things that we share in our world are far more valuable than those which divide us.</p>
-
-<h2 class="section-heading">The Final Frontier</h2>
-
-<p>There can be no thought of finishing for ‘aiming for the stars.’ Both figuratively and literally, it is a task to occupy the generations. And no matter how much progress one makes, there is always the thrill of just beginning.</p>
-
-<p>There can be no thought of finishing for ‘aiming for the stars.’ Both figuratively and literally, it is a task to occupy the generations. And no matter how much progress one makes, there is always the thrill of just beginning.</p>
-
-<blockquote class="blockquote">The dreams of yesterday are the hopes of today and the reality of tomorrow. Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next ten.</blockquote>
-
-<p>Spaceflights cannot be stopped. This is not the work of any one man or even a group of men. It is a historical process which mankind is carrying out in accordance with the natural laws of human development.</p>
-
-<h2 class="section-heading">Reaching for the Stars</h2>
-
-<p>As we got further and further away, it [the Earth] diminished in size. Finally it shrank to the size of a marble, the most beautiful you can imagine. That beautiful, warm, living object looked so fragile, so delicate, that if you touched it with a finger it would crumble and fall apart. Seeing this has to change a man.</p>
-
-<img class="img-fluid" src="https://source.unsplash.com/Mn9Fa_wQH-M/800x450" alt="Demo Image">
-<span class="caption text-muted">To go places and do things that have never been done before – that’s what living is all about.</span>
-
-<p>Space, the final frontier. These are the voyages of the Starship Enterprise. Its five-year mission: to explore strange new worlds, to seek out new life and new civilizations, to boldly go where no man has gone before.</p>
-
-<p>As I stand out here in the wonders of the unknown at Hadley, I sort of realize there’s a fundamental truth to our nature, Man must explore, and this is exploration at its greatest.</p>
-
-<p>Placeholder text by <a href="http://spaceipsum.com/">Space Ipsum</a>. Photographs by <a href="https://unsplash.com/">Unsplash</a>.</p>
diff --git a/_posts/2023-04-3-Bioinformatics.html b/_posts/2023-04-3-Bioinformatics.html
new file mode 100644
index 0000000000..1a5ec49aad
--- /dev/null
+++ b/_posts/2023-04-3-Bioinformatics.html
@@ -0,0 +1,70 @@
+---
+layout: post
+title: "Bioinformatics: high-throughput methods"
+subtitle: "High-throughput pharmacological screening consists of testing large collections of chemical compounds or natural products to identify biologically active molecules."
+date: 2023-04-3 10:45:13 -0400
+background: '/img/posts/07.avif'
+---
+
+<h2 class="section-heading">Executive Summary</h2>
+<h2>Abstract</h2>
+    <p>Dengue is an acute febrile illness caused by the Dengue virus (DENV), with a high number of cases worldwide. There is no available treatment that directly affects the virus or the viral cycle. The objective of this study was to identify a compound derived from natural products that interacts with the NS5 protein of the dengue virus through virtual screening and evaluate its in vitro antiviral effect on DENV-2.</p>
+
+    <h3>Methods</h3>
+    <p>
+        <strong>Molecular Docking:</strong> Molecular docking was performed on NS5 using AutoDock Vina software to identify potential compounds that could interact with the NS5 protein.
+    </p>
+    <p>
+        <strong>Selection of Compounds:</strong> Compounds with physicochemical and pharmacological properties of interest were selected based on the results of molecular docking.
+    </p>
+    <p>
+        <strong>Antiviral Effect Evaluation:</strong> The preliminary antiviral effect was evaluated through several methods:
+    </p>
+    <ul>
+        <li>
+            <strong>NS1 Protein Expression:</strong> The effect on viral replication was assessed by measuring the expression of the NS1 protein.
+        </li>
+        <li>
+            <strong>NS5 Production:</strong> The effect on viral genome replication and/or translation was determined by measuring NS5 production using DENV-2 Huh-7 replicon through ELISA.
+        </li>
+        <li>
+            <strong>Viral RNA Quantification:</strong> Viral RNA quantification was performed using RT-qPCR to assess the impact on viral genome replication.
+        </li>
+    </ul>
+
+    <h3>Results</h3>
+    <p>The in silico strategy proved effective in finding a compound (M78) with an indole-like structure.</p>
+    <p>
+        <strong>Antiviral Effect:</strong> Treatment with M78 at 50 µM reduced the expression of the NS5 protein by 70% and decreased viral RNA by 1.7 times, suggesting that M78 is involved in the replication and/or translation of the viral genome.
+    </p>
+</ul>
+<h2 class="section-heading">Bioinformatics: In Silico Assays</h2>
+<img class="img-fluid" src="https://www.mdpi.com/viruses/viruses-15-01563/article_deploy/html/images/viruses-15-01563-g002.png" alt="Demo Image">
+    <h2>Virtual Screening of Natural Compound Derivatives on DENV NS5 Protein</h2>
+    <p>As part of my data science experience, I conducted virtual screening of natural compound derivatives on the DENV NS5 protein. The objective of this study was to identify compounds that could interact with the NS5 protein, a key component of the Dengue virus. This research utilized cutting-edge in silico techniques to identify potential antiviral compounds.</p>
+
+    <p>The virtual screening involved:</p>
+    <ul>
+        <li>Obtaining the structures of NS5 proteins from the four DENV serotypes.</li>
+        <li>Selecting cavities for interaction, such as the substrate binding site and RNA tunnel.</li>
+        <li>Re-docking ligands SAH and 68E to obtain binding energy values.</li>
+        <li>Performing eight virtual screenings, two for each serotype.</li>
+        <li>Utilizing a library of 190,090 natural compound derivatives available on the DrugDiscovery@TACC web portal.</li>
+        <li>Employing AutoDock Vina 1.1 software for virtual screening calculations.</li>
+    </ul>
+
+    <h2>Selection of Compounds by Interaction on NS5 of DENV</h2>
+    <p>After the virtual screening, the selection of compounds was a crucial step. Compounds were chosen based on their ability to bind to NS5 in all four serotypes of the Dengue virus. The selection process involved the following criteria:</p>
+
+    <ul>
+        <li>Assessing aqueous solubility using the SwissADME web server.</li>
+        <li>Checking compliance with Lipinski's rules to evaluate drug-likeness.</li>
+        <li>Predicting possible toxicological risks using the ProTox-II web server.</li>
+        <li>3D visualization of protein-ligand complexes using the Chimera v1.13.1 program.</li>
+    </ul>
+    <p>This experience showcases the use of advanced in silico tools and computational methods to identify potential antiviral compounds, emphasizing the importance of data science in drug discovery and research.</p>
+
+<p>For more details, refer to the complete analysis and code in this repository.</p> 
+<a href="https://www.mdpi.com/1999-4915/15/7/1563" target="_blank" class="btn btn-primary">Open in MPDI</a>
+
+<p>Photographs by <a href="https://unsplash.com/">Unsplash</a>.</p>
\ No newline at end of file
diff --git a/_posts/2023-04-3-Energy Efficiency.html b/_posts/2023-04-3-Energy Efficiency.html
new file mode 100644
index 0000000000..f587f6bd51
--- /dev/null
+++ b/_posts/2023-04-3-Energy Efficiency.html	
@@ -0,0 +1,67 @@
+---
+layout: post
+title: "Energy Efficiency in Residential Buildings"
+subtitle: "Exploring the Relationship Between Building Design and Energy Efficiency"
+date: 2023-04-3 10:45:13 -0400
+background: '/img/posts/06.jpg'
+---
+
+<h2 class="section-heading">Executive Summary</h2>
+
+<p>This project focuses on the analysis and optimization of energy efficiency in residential buildings using the mentioned dataset. The physical and design characteristics of buildings will be explored to understand their impact on heating load. For this purpose, models will be developed to predict the required heating load based on specific building characteristics. Additionally, the aim is to identify key attributes that influence the energy efficiency of buildings and provide practical recommendations for optimizing the design and energy management of residential buildings.</p>
+
+<h2 class="section-heading">Description of Dataset</h2>
+
+<p><strong>Source:</strong> This dataset was created by Angeliki Xifara and Athanasios Tsanas from the University of Sheffield, United Kingdom.</p>
+
+<p>The "Energy Efficiency" dataset is available in the UCI Machine Learning Repository. The "Energy Efficiency" dataset is commonly used in regression problems to predict energy efficiency in buildings.</p>
+
+<p> <strong> Attribute Description:</strong> The "Energy Efficiency" dataset, which was collected to evaluate the energy performance of buildings, consists of 768 instances (m). Each building is represented by eight input attributes (independent variables) related to its design and geometry, and two output attributes (dependent variables).</p>
+
+<blockquote class="blockquote">Input Attributes (x):</blockquote>
+
+<ul>
+    <li>Relative Compactness: Relative compactness of the building</li>
+    <li>Surface Area: Total surface area of the building </li>
+    <li>Wall Area: Wall area of the building </li>
+    <li>Roof Area: Roof area of the building </li>
+    <li>Overall Height: Overall height of the building </li>
+    <li>Orientation: Orientation of the building </li>
+    <li>Glazing Area: Relative glazing area</li>
+    <li>Glazing Area Distribution: Distribution of glazing area </li>
+  </ul>
+<blockquote class="blockquote">Output Attributes(y):</blockquote>
+<ul> 
+    <li>Heating Load: Heating load of the building</li>
+    <li>Cooling Load: Cooling load of the building</li>
+</ul>
+<h2 class="section-heading">Exploratory Data Analysis</h2>
+<img class="img-fluid" src="https://images.unsplash.com/photo-1542903660-eedba2cda473?auto=format&fit=crop&q=80&w=2070&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" alt="Demo Image">
+
+<p><strong>Methodology and Modeling</strong> </p>
+<p>A methodology consisting of two phases was implemented: modeling and evaluation. During the modeling phase, several regression models were trained, including Linear Regression, Theil-Sen Regressor, Random Forest Regressor, Support Vector Regressor (SVR), and Decision Tree Regressor. These models were trained independently to predict both heating load and cooling load.</p>
+
+<p><strong>Modeling Results</strong> </p>
+<p>In the modeling phase, notable trends in model performance for both variables were observed:</p>
+
+<ul>
+    <li>For heating load prediction, the Random Forest Regressor model exhibited outstanding performance, with a Mean Squared Error (MSE) close to zero and an R-squared (R2) almost equal to 1. This indicates a nearly perfect fit to the training data, although overfitting is a possibility. The Linear Regression, Theil-Sen Regressor, and SVR models also showed good performance, though slightly behind the Random Forest model.</li>
+    <li>Regarding cooling load prediction, similar patterns were observed. The Random Forest Regressor model excelled with very low MSE and an R2 close to 1. The other models also demonstrated good performance, but not as remarkable as the Random Forest model.</li>
+</ul>
+
+<p><strong>Evaluation Results</strong> </p>
+<p>In the evaluation phase, the trends observed in the modeling phase were confirmed:</p>
+
+<ul>
+    <li>For heating load prediction, the Random Forest Regressor model continued to exhibit exceptional performance, with low MSE and high R2. The Decision Tree Regressor model also showed good performance in the evaluation phase.</li>
+    <li>For cooling load prediction, a similar pattern was observed, with the Random Forest Regressor model remaining the leader, followed by the Decision Tree Regressor model.</li>
+</ul>
+
+<p><strong>Conclusions and Recommendations</strong> </p>
+<p>Based on the results obtained, it can be concluded that the Random Forest Regressor model is the most promising for this specific dataset. However, it is important to remember that results may vary in different datasets, so cross-validation is recommended, and model hyperparameters should be adjusted for more robust results.</p>
+
+<p>Furthermore, it is suggested to explore other feature engineering techniques and consider the inclusion of more predictor variables to further enhance model performance. This project provides a solid foundation for energy load prediction in heating and cooling systems and can be scaled and adapted for future applications in energy efficiency in buildings.</p>
+<p>For more details, refer to the complete analysis and code in this repository.</p> 
+<a href="https://colab.research.google.com/drive/13w65VKoAwXElyx8wDCGCiW2DDPr-ZcjI?usp=sharing" target="_blank" class="btn btn-primary">Open in Google Colab</a>
+
+<p>Photographs by <a href="https://unsplash.com/">Unsplash</a>.</p>
\ No newline at end of file
diff --git a/_posts/2023-06-28-WIne Quality.html b/_posts/2023-06-28-WIne Quality.html
new file mode 100644
index 0000000000..4fa00a57dd
--- /dev/null
+++ b/_posts/2023-06-28-WIne Quality.html	
@@ -0,0 +1,62 @@
+---
+layout: post
+title: "Wine Quality Predictor"
+subtitle: "Exploring wine quality through analysis and prediction"
+date: 2023-06-28 23:45:13 -0400
+background: '/img/posts/03.jpg'
+---
+
+<p>This project aims to apply Machine Learning techniques to develop predictive models that can predict the quality of wine based on its physicochemical attributes. To achieve this, visualization and exploratory analysis techniques will be explored to understand the distribution of the attributes and their impact on prediction. Additionally, the crucial role of data preprocessing and transformation in the modeling phase will be highlighted.</p>
+
+<h2 class="section-heading">Description of Dataset</h2>
+
+<p><strong>Source:</strong> This dataset was created by P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis. The "Wine Quality" dataset is available in the UCI Machine Learning Repository.</p>
+
+<p>The "Wine Quality" dataset is commonly used in classification and regression problems to predict wine quality. It was collected to evaluate the quality of white and red wines based on physicochemical attributes. The inputs were gathered through physicochemical tests, and the output is based on sensory data obtained from evaluations conducted by wine experts. Each expert rated the wine quality on a scale from 0 (very poor) to 10 (excellent).</p>
+
+<p> <strong> Attribute Description:</strong>The "Wine Quality" dataset consists of instances of white and red wines, with a total of 11 numerical attributes. These attributes include characteristics such as acidity, sugar levels, sulfates, and alcohol content. The wine quality is represented by a categorical target variable. </p>
+
+<blockquote class="blockquote">Input Attributes (x):</blockquote>
+<ul>
+    <li>Fixed acidity (Acidez fija): g/dm³ (float)</li>
+    <li>Volatile acidity (Acidez volátil): g/dm³ (float)</li>
+    <li>Citric acid (Ácido cítrico): g/dm³ (float)</li>
+    <li>Residual sugar (Azúcar residual): g/dm³ (float)</li>
+    <li>Chlorides (Cloruros): g/dm³ (float)</li>
+    <li>Free sulfur dioxide (Dióxido de azufre libre): mg/dm³ (float)</li>
+    <li>Total sulfur dioxide (Dióxido de azufre total): mg/dm³ (float)</li>
+    <li>Density (Densidad): g/cm³ (float)</li>
+    <li>pH: (float)</li>
+    <li>Sulphates (Sulfatos): g/dm³ (float)</li>
+    <li>Alcohol (Alcohol): % vol (float)</li>
+</ul>
+  
+<blockquote class="blockquote">Output Attributes(y):</blockquote>
+<ul> 
+    <li>Wine Quality: It is evaluated on a discrete scale of 0 to 10, where a higher value indicates better quality</li>
+</ul>
+<h2 class="section-heading">Exploratory Data Analysis</h2>
+<img class="img-fluid" src="https://images.unsplash.com/photo-1542903660-eedba2cda473?auto=format&fit=crop&q=80&w=2070&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" alt="Demo Image">
+<P>Before diving into the predictive modeling, an initial step is to perform an Exploratory Data Analysis (EDA). For this purpose, you can load the dataset into your working environment, such as Python with Pandas, and execute the following tasks:</P>
+
+        <p>This repository contains the results of a comprehensive analysis of a white wine dataset provided by the reference researchers. The analysis involved three key stages: data preprocessing, modeling and evaluation, and performance improvement strategies.</p>
+    
+        <p> <strong> Data Preprocessing</strong></p>
+        <p>In the data preprocessing phase, we applied essential techniques, such as feature scaling using StandardScaler. This ensured that all features contributed equally to the modeling process, thus avoiding the dominance of certain attributes over others.</p>
+    
+        <p> <strong> Modeling</strong></p>
+        <p>In the modeling stage, we deliberately selected four specific algorithms: Decision Tree, Random Forest, SVM, and K-NN. The choice of these algorithms was based on their demonstrated ability to address classification problems in complex datasets. It is important to note that, although some algorithms were shared with the reference study, the methodologies differed in aspects such as hyperparameter tuning and how class imbalance was addressed.</p>
+        
+        <p> <strong> Evaluation</strong></p>
+        <p>The reference study provided results that served as a starting point for model evaluation. It was observed that, despite achieving acceptable levels of accuracy, class imbalance affected the model's ability to generalize to minority classes. This finding was crucial in determining the need to implement an oversampling strategy.</p>
+    
+        <p> <strong> Performance Improvement</strong></p>
+        <p>The oversampling strategy was the key element in significantly improving the model's performance. By increasing the number of instances in the minority classes, the class distribution was balanced, allowing the model to learn more effectively from all classes. This resulted in a noticeable increase in accuracy on the test set, reaching an impressive 92%.</p>
+        
+        <p> <strong> Conclusion</strong></p>
+        <p>While both methodologies shared the use of some common algorithms, they differed in their approach to addressing class imbalance and the specific model configurations. This distinction is fundamental and highlights the importance of adapting methodologies to the unique characteristics of each dataset and problem.</p>
+    
+        <p>For more details, refer to the complete analysis and code in this repository.</p> 
+        <a href="https://colab.research.google.com/drive/1CpCsCjCLkUBLazwJL5NQSS70D6kBgIbr?usp=sharing" target="_blank" class="btn btn-primary">Open in Google Colab</a>
+      
+<p>Photographs by <a href="https://unsplash.com/">Unsplash</a>.</p>
diff --git a/about.html b/about.html
index c661ef64e4..868fc631cc 100644
--- a/about.html
+++ b/about.html
@@ -4,9 +4,10 @@
 description: This is what I do.
 background: '/img/bg-about.jpg'
 ---
-
-<p>Lorem ipsum dolor sit amet, consectetur adipisicing elit. Sed quisquam ut perspiciatis, repudiandae nulla animi iste vel, praesentium repellendus molestias aliquid consequatur, earum rem qui error voluptates eius enim consequuntur!</p>
-
-<p>Lorem ipsum dolor sit amet, consectetur adipisicing elit. Ex alias, earum consectetur quia natus ducimus voluptate explicabo, hic porro reprehenderit, quasi? Tenetur ipsum distinctio laboriosam perspiciatis officiis dolore, architecto id.</p>
-
-<p class="mb-5">Lorem ipsum dolor sit amet, consectetur adipisicing elit. Totam inventore aspernatur repellendus incidunt adipisci modi voluptates recusandae iste eligendi, repudiandae corporis quod aut, optio! Explicabo quaerat unde voluptatem! Itaque, eum!</p>
+<div class="text-center">
+    <p class="mb-4">
+        <span class="d-inline-block align-middle"> As a specialized data science professional, I have a track record of over two years dedicated to scientific data analysis, especially in the biomedical field. My expertise primarily revolves around the advanced application of statistics and modeling using tools such as Python and R. Through a detailed examination of my portfolio, you can appreciate the projects I've undertaken, showcasing my abilities to address complex problems using innovative data science techniques, thereby contributing to the advancement of multidisciplinary knowledge.</span>      
+    </p>
+    <p>For more details, refer to my CV in Canvas </p> 
+    <a href="https://www.canva.com/design/DAF5mhuois0/ox2dUrBL1Oj7KT3EJQEmNw/view?utm_content=DAF5mhuois0&utm_campaign=designshare&utm_medium=link&utm_source=editor" target="_blank" class="btn btn-primary">Open in Canvas</a>
+</div>
diff --git a/contact.html b/contact.html
index e23d22616e..09d37786db 100644
--- a/contact.html
+++ b/contact.html
@@ -6,39 +6,32 @@
 form: true
 ---
 
-<p>Want to get in touch? Fill out the form below to send me a message and I will get back to you as soon as possible!</p>
-<form name="sentMessage" id="contactForm" novalidate>
-  <div class="control-group">
-    <div class="form-group floating-label-form-group controls">
-      <label>Name</label>
-      <input type="text" class="form-control" placeholder="Name" id="name" required data-validation-required-message="Please enter your name.">
-      <p class="help-block text-danger"></p>
+<div class="container">
+  <div class="row">
+    <div class="col-lg-8 col-md-10 mx-auto">
+      <p class="text-center mb-4">Connect with me:</p>
+      <ul class="list-inline text-center">
+        <li class="list-inline-item">
+          <a href="https://linkedin.com/in/cindy-johanna-diaz-aguirre" target="_blank" class="d-inline-block">
+            <i class="fab fa-linkedin fa-3x"></i>
+          </a>
+        </li>
+        <li class="list-inline-item">
+          <a href="https://github.com/Cjohanna/Cjohanna.github.io" class="d-inline-block">
+            <i class="fab fa-github fa-3x"></i>
+          </a>
+        </li>      
+        <li class="list-inline-item">
+          <a href="diazaguirrejohanna@gmail.com" class="d-inline-block">
+            <i class="fas fa-envelope fa-3x"></i>
+          </a>
+        </li>
+        <li class="list-inline-item">
+          <a href="https://www.kaggle.com/tuusuario" class="d-inline-block">
+            <i class="fab fa-kaggle fa-3x"></i>
+          </a>
+        </li>
+      </ul>
     </div>
   </div>
-  <div class="control-group">
-    <div class="form-group floating-label-form-group controls">
-      <label>Email Address</label>
-      <input type="email" class="form-control" placeholder="Email Address" id="email" required data-validation-required-message="Please enter your email address.">
-      <p class="help-block text-danger"></p>
-    </div>
-  </div>
-  <div class="control-group">
-    <div class="form-group col-xs-12 floating-label-form-group controls">
-      <label>Phone Number</label>
-      <input type="tel" class="form-control" placeholder="Phone Number" id="phone" required data-validation-required-message="Please enter your phone number.">
-      <p class="help-block text-danger"></p>
-    </div>
-  </div>
-  <div class="control-group">
-    <div class="form-group floating-label-form-group controls">
-      <label>Message</label>
-      <textarea rows="5" class="form-control" placeholder="Message" id="message" required data-validation-required-message="Please enter a message."></textarea>
-      <p class="help-block text-danger"></p>
-    </div>
-  </div>
-  <br>
-  <div id="success"></div>
-  <div class="form-group">
-    <button type="submit" class="btn btn-primary" id="sendMessageButton">Send</button>
-  </div>
-</form>
+</div>
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diff --git a/posts/index.html b/posts/index.html
index d4876b74ed..02ea6b8fcd 100644
--- a/posts/index.html
+++ b/posts/index.html
@@ -1,6 +1,6 @@
 ---
 layout: page
-title: Posts
+title: Projects
 background: '/img/bg-post.jpg'
 ---
 
@@ -15,13 +15,7 @@ <h3 class="post-subtitle">{{ post.subtitle }}</h3>
     <h3 class="post-subtitle">{{ post.excerpt | strip_html | truncatewords: 15 }}</h3>
     {% endif %}
   </a>
-  <p class="post-meta">Posted by
-    {% if post.author %}
-    {{ post.author }}
-    {% else %}
-    {{ site.author }}
-    {% endif %}
-    on {{ post.date | date: '%B %d, %Y' }} &middot; {% include read_time.html content=post.content %}
+  <p class="post-meta">Posted by Johanna Diaz
   </p>
 </article>