Personalised_Product_Recomendation_System

Overview

This project is a Product Recommendation System designed to provide users with personalized shopping experiences. It integrates user-based and content-based recommendation techniques along with a deep learning model for product recommendation, to suggest relevant products based on user interactions and preferences. The system is implemented as a web application using Flask.

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Features

1. User Authentication:
   • Users can log in and access their personalized recommendations.
2. Content-Based Recommendations:
   • Suggests products similar to the one currently being viewed.
3. User-Based Recommendations:
   • Recommends products based on the preferences of similar users.
4. Product Search:
   • Allows users to search for products by keywords.
5. Interactive Dashboard:
   • Displays trending products and user-specific suggestions.

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Technology Stack

• Frontend:
  • HTML/CSS for user interface design
  • JavaScript for interactive elements
• Backend:
  • Python and Flask for server-side logic
• Data Processing:
  • Pandas and NumPy for data manipulation
  • Scikit-learn for recommendation model development
• Recommendation Techniques:
  • TF-IDF for content similarity
  • TruncatedSVD for collaborative filtering
• Database:
  • SQL for storing user-product interactions
  • CSV files for product and user data

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Installation

1. Clone the repository:

   git clone https://github.com/your-repo/product-recommendation-system.git

2. Navigate to the project directory:

   cd product-recommendation-system

3. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

4. Install dependencies:

   pip install -r requirements.txt

5. Set up environment variables:

   • Create a .env file in the project root and add the following:

     FLASK_SECRET_KEY=your_secret_key

6. Run the Flask application:

   flask run

7. Open the application in your browser:

   • Navigate to http://127.0.0.1:5000/.

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File Structure

project-directory/
|-- static/
|   |-- img/          # Images for products and UI
|-- templates/
|   |-- *.html        # HTML templates for pages
|-- models/
|   |-- *.csv         # Product and user data files
|-- app.py            # Main application file
|-- requirements.txt  # Python dependencies
|-- .env              # Environment variables

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Usage

1. Login: Enter a valid user ID to access recommendations.
2. Search Products: Use the search bar to find products.
3. View Similar Products: Click on a product to see content-based recommendations.

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Future Enhancements

1. Expand product categories and data sources.
2. Include user feedback for improving recommendations.
3. Implement real-time data updates.

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Contributors

• Ujair Shaha (GitHub Profile)

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