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Music Recommender System

Description

This repository contains the code for a music recommender system for Radio Javan. The system is designed to suggest songs based on user preferences and listening history. It utilizes TF-IDF for text analysis and incorporates various music features such as danceability, energy, and more.

Table of Contents

Installation

Prerequisites

  • Python 3.6 or higher
  • Jupyter Notebook

Steps

  1. Clone the repository
    git clone https://github.com/parvvaresh/Music-recommender-system.git
  2. Navigate to the project directory
    cd Music-recommender-system
  3. Create and activate a virtual environment
    python3 -m venv venv
    source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
  4. Install the required packages
    pip install -r requirements.txt

Usage

Jupyter Notebook

  1. Start Jupyter Notebook
    jupyter notebook
  2. Open the .ipynb file in the Jupyter interface to run the code cells interactively.

Running the Script

  1. Ensure your virtual environment is activated
  2. Run the Python script
    python script_name.py

Contributing

Contributions are welcome! Please follow these steps to contribute:

  1. Fork the repository
  2. Create a new branch (git checkout -b feature/YourFeature)
  3. Commit your changes (git commit -m 'Add some feature')
  4. Push to the branch (git push origin feature/YourFeature)
  5. Create a new Pull Request

License

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

Acknowledgements

  • Any libraries, datasets, or tutorials that were particularly helpful.
  • Any contributors or collaborators.

Features

  • TF-IDF for Text Analysis: Utilizes Term Frequency-Inverse Document Frequency (TF-IDF) to analyze textual data.
  • Music Features: Incorporates various music features such as danceability, energy, and more to enhance recommendations.

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This repository contains the code for a music recommender system for Radio Javan.

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  • Jupyter Notebook 99.6%
  • Python 0.4%