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Personalised E-Commerce Product Recommendation System

Overview

This project aims to create a personalized product recommendation system for e-commerce platforms. It leverages user interaction data, purchase history, and product information to generate tailored recommendations, enhancing the shopping experience and potentially increasing sales.

Features

  • Data Exploration and Preprocessing: Jupyter notebooks for exploring, cleaning, and preparing the dataset for the recommendation algorithm.
  • Recommendation Algorithm: Implementation of an interest calculation algorithm to score user interest in products based on past interactions and purchases.
  • Web Application: A Flask web application to showcase the recommendation system in action, allowing users to browse products, view recommendations, and simulate purchases.

Project Structure

  • Code For Recommendation System/: Contains all the Jupyter notebooks and CSV files related to the recommendation algorithm.
    • Data Exploration.ipynb: Notebook for initial data exploration.
    • Data Generation.ipynb: Notebook for generating synthetic data for testing.
    • Interest Calculation-Algorithm.ipynb: Notebook detailing the algorithm for calculating user interest scores.
    • Recommendation.ipynb: Notebook for generating product recommendations.
    • CSV files: Include user.csv, product.csv, purchases.csv, interactions.csv, interest_scores.csv, and all_recommendations.csv for algorithm inputs and outputs.
  • Code for Website to showcase Recommendation System/: Contains the Flask application and static assets.
    • app.py: The Flask application.
    • templates/: HTML templates for the web interface.
    • static/: CSS and image files for the web application.
    • CSV files: Same as above, used by the web application to display recommendations.
  • Proof of Concept PPT.pdf: A presentation outlining the concept and implementation of the project.
  • Report-Personalised Product Recommendation System.pdf: A detailed report on the project, including methodology, results, and conclusions.

Getting Started

To run the recommendation system:

  1. Clone the repository.
  2. Navigate to the Code For Recommendation System/ directory to explore the Jupyter notebooks.
  3. To run the web application:
    • Ensure you have Python and Flask installed.
    • Navigate to the Code for Website to showcase Recommendation System/ directory.
    • Run app.py using Flask.
    • Access the web application at http://localhost:5000.

Technologies Used

  • Python: For data processing and the web server.
  • Jupyter Notebook: For data exploration and algorithm development.
  • Flask: For the web application.
  • HTML/CSS: For the web interface.

Contributing

Contributions to improve the recommendation system or the web application are welcome. Please fork the repository, make your changes, and submit a pull request.