https://github.com/its-maneeshk/fake-product-detection-system

The Fake Product Review Detection System is a machine learning-powered web application designed to analyze and detect fake reviews on eCommerce platforms. It helps users identify whether a product has genuine or manipulated reviews by leveraging Natural Language Processing (NLP) and supervised learning models.
https://github.com/its-maneeshk/fake-product-detection-system

api beautifulsoup4 fetch-api flask html-css-javascript joblib nlp-machine-learning numpy pandas python reactjs requests scikit-learn

Last synced: 7 months ago
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Host: GitHub
URL: https://github.com/its-maneeshk/fake-product-detection-system
Owner: its-maneeshk
Created: 2025-02-07T18:14:49.000Z (8 months ago)
Default Branch: main
Last Pushed: 2025-03-02T11:15:18.000Z (7 months ago)
Last Synced: 2025-03-02T12:20:39.297Z (7 months ago)
Topics: api, beautifulsoup4, fetch-api, flask, html-css-javascript, joblib, nlp-machine-learning, numpy, pandas, python, reactjs, requests, scikit-learn
Language: JavaScript
Homepage: https://fake-product-detection-system.vercel.app
Size: 30.3 MB
Stars: 1
Watchers: 1
Forks: 3
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

# 🚀 Fraud Filter - Fake Product Detection System

**Fake Product Review Detection System 📌**
The **Fake Product Detection System** is a **machine learning-powered web application** that helps users identify whether a product has **genuine or manipulated reviews** on eCommerce platforms. It leverages **Natural Language Processing (NLP)** and **supervised learning models** to analyze reviews and detect fake ones.

---
## 🌟 Features

| **Feature** | **Description** |
|---------------------------------|----------------------------------------------------------------------------------------------------------|
| **Fake Review Detection** | Classifies reviews as **Fake** or **Original** using a trained ML model. |
| **User-Friendly Interface** | Simple, intuitive UI built with **React & Tailwind CSS**. |
| **API Integration** | Connects to a **Flask backend** for real-time predictions. |
| **Data Upload Support** | Allows users to upload **CSV datasets** for batch analysis. |
| **Visualization & Insights** | Displays **review authenticity percentage** with meaningful insights. |
| **Fast & Efficient Processing** | Uses **vectorization techniques** for quick text analysis. |

---

## 🛠 Tech Stack

✅ **Frontend:** Vite + React + Tailwind CSS
✅ **Backend:** Flask (REST API)
✅ **Machine Learning:** Scikit-learn (Logistic Regression)
✅ **Model Storage:** Joblib for saving/loading `.pkl` models
✅ **Data Processing:** Pandas & NumPy

---

## 📌 How It Works ?

1️⃣ **Train the Machine Learning model** using real & fake review datasets.
2️⃣ **Save the trained model** as `fake_review_model.pkl`.
3️⃣ **Run the Flask backend server** to expose a REST API.
4️⃣ **Connect the React frontend** to interact with the API.
5️⃣ **Upload or enter product reviews** to get authenticity results.

⚡ **This system empowers consumers to make informed purchasing decisions by identifying fraudulent product reviews!**

---

## 📂 Project Directory Structure

```sh
FraudFilter - Minor Project/
│── backend/
│ ├── .venv/ # Virtual environment (version = 3.13.2)
│ ├── ml/ # ML-related scripts and utilities
│ ├── model/ # Trained ML models
│ ├── scraped_files/ # Stores scraped eCommerce reviews
│ ├── uploads/ # Stores uploaded files for analysis
│ ├── utils/ # Helper functions for backend
│ ├── app.py # Main Flask API file
│ ├── requirements.txt # Python dependencies
│── frontend/
│ ├── node_modules/ # Dependencies for frontend
│ ├── public/ # Public assets like index.html
│ ├── src/ # React source files
│ │ ├── components/ # Reusable React components
│ │ ├── assets/ # Images, icons, etc.
│ │ ├── utils/ # Utility functions
│ ├── .env # Environment variables
│ ├── .gitignore # Git ignore file
│ ├── eslint.config.js # ESLint configuration
│ ├── index.html # Main HTML file
│ ├── package.json # Frontend dependencies
│ ├── package-lock.json # Lockfile for package versions
│ ├── postcss.config.js # PostCSS configuration
│ ├── README.md # Project documentation
│ ├── tailwind.config.js # Tailwind configuration
│ ├── vite.config.js # Vite configuration
```
---
## 📦 Installation & Setup

### 🔹 Prerequisites
Ensure you have the following installed on your system:
- **Python 3.8+**
- **Node.js & npm**
- **pip** (Python package manager)

---

### 🔹 Backend Setup (Flask API)
```sh
# Navigate to the backend folder
cd backend

# Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run the Flask server
python app.py

```
#### 🚀 Flask API will start at http://127.0.0.1:5000/

### 🔹 Frontend Setup (React + Vite + Tailwind CSS)
```sh
# Navigate to the frontend folder
cd frontend

# Install dependencies
npm install

# Start the development server
npm run dev
```
#### 🚀 React app will run at http://localhost:5173/
---

## 📸 Demo Screenshots

| **Interface** | **Preview** |
|--------------|------------|
| **HOME** | |
| **WORKING** | |
| **ABOUT** | |
| **CONTACTS** | |
| **BLOGS** | |
| **FAQ's** | |
| **MODEL TRY PAGE** | |
| **USING CSV FILE** | |
| **USING PRODUCT LINK** | |

📌 More detailed **UI screenshots** can be found in the project_images/ folder.

---

## 🔗 Contributing

💡 **Want to contribute?** Fork the repo, create a branch, and submit a pull request. I welcome **bug fixes, feature improvements, and optimizations**.

---

## 📬 Contact

💻 **Developed by [Manish Patel](https://github.com/its-maneeshk)**

📧 **Email:** [maneeshkurmii@gmail.com](mailto:maneeshkurmii@gmail.com)
🔗 **LinkedIn:** [itsmaneeshk](https://www.linkedin.com/in/itsmaneeshk/)
📷 **Instagram:** [its_maneeshk_](https://www.instagram.com/its_maneeshk_/)

---

## 🏆 Tech Badges