E-commerce Product Recommendation System

Project Overview

This project implements a product recommendation system for an e-commerce platform. The system utilizes collaborative filtering, content-based filtering and hybird approach techniques to provide personalized product recommendations to users based on their purchase history.

Features

• Collaborative Filtering: Recommends products based on the purchase behavior of similar users.
• Content-Based Filtering: Recommends products based on the attributes of the products the user has previously purchased.
• Hybrid Approach: Recommends products by combining Collaborative and Content-Based Filtering.
• User-Friendly Interface: A web application built with Flask for easy interaction and recommendations.

Technologies Used

• Python: For backend logic and data processing.
• Flask: Web framework for creating the web application.
• Pandas: For data manipulation and analysis.
• HTML/CSS: For frontend design.

Getting Started

Prerequisites

• Python 3.x
• Flask
• Pandas

Installation

1. Clone the repository:

   git clone <repository-url>

2. Navigate to the project directory:

   cd er

3. Install the required packages:

   pip install flask pandas

Running The Application

1. Start the Flask server:

   python app.py

2. Open your web browser and navigate to http://127.0.0.1:5000.

Data Files

1. users.csv: Contains user information.
2. products.csv: Contains product details such as name, category, description, price, and rating.
3. purchases.csv: Contains user purchase history.
4. browsing_history: Contains user browsing history.

Usage

1. Enter a user ID and select a recommendation method (collaborative, content-based or hybrid approach).
2. Click on "Get Recommendations" to view the recommended products.

Screenshots

Contributing

If you would like to contribute to this project, please fork the repository and submit a pull request.

Future Enhancements

• More Data: I plan to add more file to enhance the model aspect of our recommendation system.

• Clean UI: Improving the user interface (UI) for my e-commerce product recommendation system can significantly enhance user experience and engagement.

• Feedback Mechanism: Implement a way for users to provide feedback on the recommendations, which can help me refine the system.

• Explainability: Work on explaining the reasoning behind recommendations, which can enhance user trust and satisfaction.