Music Recommendation System
Overview
This Music Recommendation System is designed to provide personalized song recommendations using collaborative filtering techniques. The system leverages the Surprise library in Python, utilizing algorithms such as Singular Value Decomposition (SVD) to predict user preferences and suggest songs they are likely to enjoy.
Features
Personalized Recommendations: Suggests songs tailored to each user's preferences.
Efficient Prediction: Implements the SVD algorithm for accurate rating predictions.
Cross-Validation: Evaluates model performance using metrics like RMSE and MAE.
Dynamic Recommendations: Generates top recommendations for any user based on their interaction history.
Installation
Clone this repository:
git clone
Install the required dependencies:
pip install scikit-surprise pandas
How It Works
Load Dataset: The system reads a dataset containing user interactions with songs, including user IDs, song IDs, and play counts.
Data Preparation: Formats the data for compatibility with the Surprise library.
Model Training: Splits the dataset into training and testing sets, then trains an SVD model.
Evaluation: Measures model accuracy using RMSE and performs cross-validation.
Recommendations: Generates top song recommendations for a specified user.
Usage
Dataset
The dataset should be a CSV file with the following columns:
user_id: Unique identifier for users.
song_id: Unique identifier for songs.
play_count: Number of times a user played a song.
Running the Script
Place your dataset in the project directory and update the file_path variable:
file_path = "music_dataset.csv"
Run the script:
python Music_Suggestion_System.py
View the sample dataset, model accuracy, and song recommendations in the console output.
Example
random_user = uploaded_data['user_id'].sample().iloc[0] recommendations = recommend_songs(random_user, uploaded_data, model) for song, score in recommendations: print(f"Song: {song}, Predicted Score: {score}")
Dependencies
Python 3.7+
pandas
scikit-surprise
Results
The system evaluates performance using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), ensuring robust and reliable recommendations.
Future Enhancements
Integrate with streaming platforms for real-time recommendations.
Expand dataset to include user demographics and song metadata for hybrid recommendations.
Implement a user interface for seamless interaction.
License
This project is open-source and available under the MIT License.
Acknowledgments
Special thanks to the developers of the Surprise library for enabling easy implementation of recommendation algorithms.