GitHub - IBIZZI-Khalid/Data-Harvest-Ecommerce-Data-Collector-analyzer-: Data Harvest is a tool that helps you make informed purchasing decisions. We analyze product reviews from major e-commerce sites to provide you with detailed information, sentiment analysis, and key insights. Our goal is to simplify your shopping experience and help you find the best products.

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Data Harvest is a tool that helps you make informed purchasing decisions. We analyze product reviews from major e-commerce sites to provide you with detailed information, sentiment analysis, and key insights. Our goal is to simplify your shopping experience and help you find the best products.

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Data Harvest: Make Informed Purchases with AI-Powered Reviews

Data Harvest empowers you to make smarter shopping decisions by analyzing product reviews from major e-commerce platforms like Amazon, eBay, and AliExpress. Simply enter a product name to access:

Detailed product information
Sentiment analysis of customer reviews
Identification of key pros and cons

This user-friendly tool utilizes web scraping and natural language processing to bring you real-time data and valuable insights. Built with React.js, Django, and MongoDB, Data Harvest provides a seamless experience to help you choose the right product with confidence.

Key Features:

Multi-Platform Search: Find products across various e-commerce sites.
Comprehensive Data: Access product details, reviews, and pricing in one place.
Real-Time Updates: Get the latest information with cutting-edge scraping techniques.
Intuitive Interface: Navigate and interact with ease using our user-friendly design.
Advanced Analysis: Gain deeper insights through backend processing and visualization.

Getting Started:

For easy setup, simply clone the Data Harvest repository and modify the scraping script paths (located in `backend\api\scrappers`).

Contributing:

We value your input! Feel free to contribute to Data Harvest and be part of our growing community.

Running the Project:

Backend:

1. Open a terminal in the `backend` directory.
2. Set execution policy (Windows only): `Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass`
3. Activate virtual environment: `VirtualEnv\Scripts\activate`
4. Run server: `(VirtualEnv) py manage.py runserver`

Frontend:

1. Open a terminal in the `frontend` directory.
2. Install dependencies: `npm install`
3. Start development server: `npm run dev`