Welcome to the Music Recommendation System project! This project aims to provide personalized music recommendations to users based on their listening history and preferences. Utilizing advanced machine learning techniques and a rich dataset of songs, we strive to enhance the music listening experience by suggesting tracks that users are likely to enjoy.
The Music Recommendation System leverages a dataset containing information about songs, including artist names, song titles, and lyrics. Through text preprocessing and feature extraction, the system analyzes the content and context of each song to understand its characteristics and themes. By applying machine learning algorithms, it identifies patterns and similarities between songs, enabling it to recommend music that matches the user's taste.
- Personalized Recommendations: Tailored music suggestions based on user preferences and listening history.
- Large Dataset: A comprehensive collection of songs across various genres and artists.
- Text Analysis: Advanced text processing techniques to analyze song lyrics and identify themes.
- User Interface: A simple and intuitive interface for users to interact with the recommendation system.
The dataset spotify_millsongdata.csv is required to run some parts of this project. You can obtain this dataset from the release section of this repository. Follow the steps below to download it:
- Navigate to the Releases section of this repository.
- Look for the latest release containing the dataset.
- In the assets of the release, find
spotify_millsongdata.csv. - Click on the file name to download it.
Once downloaded, place the file in the appropriate directory as required by the project setup instructions.
To set up the Music Recommendation System on your local machine, follow these steps:
- Clone the repository: git clone https://github.com/xncoder01/Music-Recommendation.git
- Navigate to the project directory: cd Music-Recommendation
- Install the required dependencies: pip install -r requirements.txt
To start the recommendation system, run the following command in the project directory:
python manage.py runserver
Navigate to http://localhost:8000 in your web browser to access the Music Recommendation System.
Contributions to the Music Recommendation System are welcome! If you have suggestions for improvements or new features, feel free to create an issue or submit a pull request.
- Thanks to all the contributors who have helped in building and enhancing this project.
- Special thanks to the open-source community for providing the tools and libraries used in this project.
For any queries or further information, please contact us at xncoder01's GitHub.