🕵️‍♀️ Fake Product Review Detection using Supervised Machine Learning

This project aims to detect fake product reviews using Natural Language Processing (NLP) and Supervised Machine Learning algorithms. It leverages text-based features and evaluates multiple classifiers to identify deceptive or spam-like reviews from genuine ones.

🧠 Algorithms Used

• Support Vector Machine (SVM) ✅ Best performing model
• Random Forest
• XGBoost
• Naive Bayes
• K-Nearest Neighbors (KNN)
• Decision Tree
• Stochastic Gradient Descent (SGD)

🛠️ Tech Stack

• Python 🐍
• Scikit-learn
• Pandas
• Numpy
• Matplotlib / Seaborn
• Natural Language Toolkit (NLTK)

📝 Features

• Preprocessing of textual data (tokenization, lemmatization, stop-word removal)
• TF-IDF Vectorization
• Comparative analysis of model performance (Accuracy, Precision, Recall, F1 Score, ROC-AUC)
• Real-time user review input testing

📊 Results

Model	Accuracy	Precision	Recall	F1 Score	ROC-AUC
SVM	85.6%	0.861	0.852	0.857	0.856
XGBoost	83.2%	0.819	0.855	0.837	0.832
Naive Bayes	83.4%	0.857	0.804	0.830	0.834
Random Forest	82.9%	0.860	0.788	0.823	0.829
KNN	55.0%	0.856	0.127	0.222	0.552

🧪 Sample Flow

1. User inputs a product review
2. Preprocessing is applied (cleaning + TF-IDF)
3. Model predicts whether the review is Fake or Genuine

🧾 Dataset

A labeled dataset of product reviews (genuine/fake) was used. Each review includes:

• Review_Text
• Label (1 = Genuine, 0 = Fake)

Note: Dataset is not included due to licensing. Please use public datasets or your own for replication.

🏗️ Project Setup

✅ Prerequisites

• Python 3.8+
• pip (Python package manager)

🔧 Setup Commands

# Clone the repo
git clone https://github.com/yourusername/FakeReviewDetection.git
cd FakeReviewDetection