Music Recommendation System

This project showcases a Music Recommendation System that uses machine learning algorithms to suggest songs based on user preferences, listening history, and popular trends. The system helps users discover new music tailored to their tastes.

Features

• Personalized song recommendations based on listening habits and user preferences.
• Collaborative filtering: Analyzes similarities between users to suggest songs.
• Content-based filtering: Recommends songs based on audio features like genre, tempo, and mood.
• Hybrid recommendation model: Combines collaborative and content-based filtering for accurate results.
• Real-time updates: Adapts to changing user tastes and current music trends.

Installation

To set up the Music Recommendation System on your local machine, follow these steps:

1. Clone the Repository

   git clone https://github.com/username/music-recommender.git
   cd music-recommender

2. Set Up Virtual Environment (Optional but Recommended)

   python3 -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install Required Packages

   pip install -r requirements.txt

Usage

To use the Music Recommendation System, start the application and begin exploring personalized music suggestions based on your listening habits:

python app.py

Access the application through your web browser to start receiving song recommendations that match your musical tastes.

Results

Experience personalized music suggestions that match your listening history and current trends, enhancing your music discovery journey with relevant song recommendations.