🧠 Fake Review Detection using Ensemble Learning

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📌 Overview

A machine learning project designed to detect fake product reviews using an ensemble of Logistic Regression and Naïve Bayes models.
Built to improve online review reliability by identifying automatically generated or spammed reviews with high accuracy.

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🚀 Features

• 🧩 Trains and evaluates multiple classification models using scikit-learn
• 🧠 Implements a stacked ensemble combining Logistic Regression & Naïve Bayes
• 💬 Utilizes TF-IDF for text vectorization and feature extraction
• 🌐 Interactive Streamlit interface for real-time single or bulk review analysis
• 📦 Deployable using ngrok for remote demo access

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🗂️ Dataset

• Size: 40,000 reviews (20K genuine, 20K AI-generated)
• Type: Text-based, CSV format
• Processing: Cleaned and preprocessed using Pandas and scikit-learn utilities

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🛠️ Tech Stack

Python • scikit-learn • Pandas • NumPy • Streamlit • TF-IDF

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📊 Results

• ✅ Achieved 92.16% accuracy on test data using the stacked ensemble
• 🔍 Ensemble model outperformed individual base learners in precision and recall
• 🧮 Reduced misclassification through text normalization and feature weighting

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🧩 How to Run Locally

1. Clone this repository

   git clone https://github.com/sudhee302/Fake-Review-Detection-using-Stacking-Ensemble
   cd Fake-Review-Detection-using-Stacking-Ensemble