Fake Review Detection System using NLP and ML

🔍 Introduction

In the age of online shopping, product reviews play a crucial role in influencing customer decisions. However, not all reviews are genuine — some are fake, either to promote products or sabotage competitors. To tackle this issue, we developed a Fake Review Detection System using Natural Language Processing (NLP) and Machine Learning, wrapped in a user-friendly Streamlit web interface.

This project enables users to upload a CSV file of product reviews and receive two separate downloadable files: one containing real reviews and the other with fake reviews.

🎓 What You Will Learn

• How to preprocess review data
• How to train an NLP-based ML model
• How to classify fake vs. real reviews
• How to create a web interface using Streamlit
• How to handle file upload and download in a web app

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🏠 Tech Stack

• Frontend: Streamlit (Python-based web framework)
• Backend: Logistic Regression with TF-IDF Vectorizer
• Language: Python
• Libraries: Pandas, NumPy, scikit-learn, re, string

🌐 Streamlit App Flow

2. NLP Model Processes Reviews

The system preprocesses the text (lowercasing, punctuation and digit removal) and uses a pre-trained TF-IDF + Logistic Regression model to classify reviews.

3. Download the Results

Two downloadable CSVs are generated: real_reviews.csv and fake_reviews.csv.

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📂 Required CSV Format

Ensure your file follows this structure:

• category: Product category
• rating: Star rating (1-5)
• label: CG for genuine, OR for other
• text_: The review content

🔮 How Fake/Real Is Determined

We used TF-IDF (Term Frequency-Inverse Document Frequency) to transform text data into numerical vectors. Then, we trained a Logistic Regression model using labeled data:

• Label CG is treated as real (1)
• Others are treated as fake (0)

🌐 Full Streamlit Code Overview

The app is a single Python file:

• Loads and trains a model using a sample dataset
• Allows CSV upload
• Validates column structure
• Preprocesses text
• Classifies reviews
• Generates download buttons for real and fake reviews

🚀 How to Run the App

1. Save the app as fake_review_app.py
2. Install dependencies:

pip install streamlit pandas numpy scikit-learn

3. Run the Streamlit app:

streamlit run fake_review_app.py

4. Open your browser at http://localhost:8501
5. Upload your review CSV and download results!

Report

The report will include:

✅ Abstract
✅ Introduction (Overview, Problem Statement, Motivation)
✅ Literature Review
✅ Existing System & Drawbacks
✅ Proposed System
✅ System Architecture (Diagrams)
✅ System Specifications
✅ Experimental Design Diagrams
✅ Implementation (Setup, Modules, Sample Code)
✅ System Testing
✅ Results & Screenshots
✅ Conclusion & Future Scope
✅ References