MrMDrX / ProductRecommender

This repository contains the code for a Product Recommendation System. The system leverages various recommendation approaches, including rank-based recommendations, collaborative filtering, and model-based collaborative filtering using Singular Value Decomposition (SVD).

Product Recommendation System

Introduction

This repository contains the code for a Product Recommendation System. The system leverages various recommendation approaches, including rank-based recommendations, collaborative filtering, and model-based collaborative filtering using Singular Value Decomposition (SVD).

Project Structure

• ProductRecommender.ipynb: The main Jupyter Notebook file containing the project code.
• data/: Directory containing the dataset file (you can download dataset from here ratings_electronics.csv.
• requirements.txt: File specifying the Python dependencies for the project.
• Presentation - Product Recommendation System.pdf: Presentation about the project

Dataset

I have used an amazon dataset on user ratings for electronic products, this dataset doesn't have any headers. To avoid biases, each product and user is assigned a unique identifier instead of using their name or any other potentially biased information.

• You can find the dataset here - https://www.kaggle.com/datasets/vibivij/amazon-electronics-rating-datasetrecommendation/download?datasetVersionNumber=1

• You can find many other similar datasets here - https://jmcauley.ucsd.edu/data/amazon/

Instructions for Running the Notebook

1. Clone this repository to your local machine.

   git clone https://github.com/MrMDrX/ProductRecommender.git

2. Navigate to the project directory.

   cd ProductRecommender

3. Install the required dependencies.

   pip install -r requirements.txt

4. Open and run the ProductRecommender.ipynb notebook in Jupyter.

Code Usage

• The notebook is organized into sections covering data exploration, preprocessing, and three recommendation approaches.
• Explore each section to understand the implementation details of the rank-based recommendation, user-based collaborative filtering, and model-based collaborative filtering using SVD.

Dependencies

• Python 3.7+
• Libraries: pandas, numpy, scikit-learn, matplotlib

Results

The project successfully generates product recommendations based on user preferences. Key findings and results are documented within the notebook.

Next Steps

• Explore additional datasets for a more comprehensive evaluation.
• Implement a web API for deploying the recommendation system.

License

This project is licensed under the MIT License - see the LICENSE file for details.