🎵 Music Recommendation System

An advanced Music Recommendation System powered by data science and a comprehensive Spotify dataset. This project leverages real-world song data, unsupervised learning, and similarity metrics to deliver personalized music recommendations.

🌟 Overview

This project demonstrates the application of data science to solve real-world problems in the music industry. By preprocessing, clustering, and analyzing song data, the system provides recommendations for users based on the similarity of features like danceability, energy, and tempo.

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🌟 Project Highlights

• Real Data: The dataset includes detailed song-level attributes sourced from Spotify, providing insights into audio characteristics.
• Unsupervised Learning: Implements KMeans Clustering for grouping similar songs.
• Similarity-Based Recommendations: Uses cosine similarity to recommend songs closest to user preferences.
• Interactive Interface: A clean and user-friendly Streamlit web application for seamless user interaction.
• Data Visualization: Insightful visualizations showcasing clustering, feature correlations, and distributions.

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🚀 How It Works

1. Data Collection:

   • The dataset contains song features like danceability, energy, tempo, and more.
   • Metadata such as song names, artists, popularity, and release year are included for richer insights.

2. Data Processing:

   • Standardized numerical features to ensure uniformity using StandardScaler.
   • Dimensionality reduction using PCA for noise reduction and computational efficiency.

3. Unsupervised Learning:

   • KMeans Clustering groups songs with similar attributes.
   • Optimal clusters are determined using the Elbow Method.

4. Recommendation Generation:

   • Cosine Similarity measures the closeness of songs within a cluster.
   • Recommendations are ranked by similarity score.

5. Interactive User Interface:

   • Built with Streamlit for a dynamic and easy-to-use app.
   • Users input a song name to get recommendations based on dataset attributes.

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📂 Project Components

• Data Preprocessing and Clustering:

  • File: Music_Clustering_and_Recommendation.py
  • Steps include data cleaning, normalization, clustering, and model saving.

• Recommendation Engine:

  • File: recommendation_engine.py
  • Implements the recommendation logic with a user-friendly Streamlit interface.

• Saved Artifacts:

  • preprocessed_data.csv: Preprocessed music data ready for recommendations.
  • kmeans_model.pkl: Trained KMeans model for clustering.

🚀 How to Run the Project

Prerequisites

Ensure the following are installed:

• Python 3.8 or higher
• Libraries: Streamlit, Pandas, Scikit-learn, Matplotlib, Seaborn, Plotly

Steps

1. Clone the repository:

   git clone https://github.com/your-username/music-recommendation-system.git
   cd music-recommendation-system

2. Run the Streamlit app:

   streamlit run recommendation_engine.py

3. Open the URL (usually http://localhost:8501) in your browser.

4. Enter a song name and get data-driven recommendations!

📊 Data Features

Features in the Spotify Dataset

• Audio Features:
  • Danceability, Energy, Acousticness, Instrumentalness, Loudness, Speechiness, Liveness, Valence, Tempo
• Popularity: Reflects the song’s global reach.
• Metadata: Duration (in milliseconds), release year, song name, and artist.

Preprocessing:

• Standardized numerical features for uniform scaling.
• Added a decade feature for exploratory analysis.

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🖼️ Visualizations

Clustering Visualization (PCA):

Clusters are visualized using Principal Component Analysis (PCA), showing clear separations among song groups. Here's an example of the clustering output:

Clustering Visualization (t-SNE):

To further understand cluster separations, we used t-SNE for high-dimensional data visualization:

Streamlit Interface:

A snapshot of the interactive Streamlit interface where users can input a song name and view recommendations:

Feature Correlations:

A correlation heatmap highlights the relationships between features, enabling effective feature selection:

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🤖 Data Science Techniques

Dimensionality Reduction:

• PCA and t-SNE for reducing noise and improving cluster interpretability.

Unsupervised Learning:

• KMeans for clustering songs based on similar features.

Similarity Metrics:

• Cosine similarity for ranking song recommendations.

Model Evaluation:

• Silhouette Scores and Elbow Method for validating clustering performance.

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🔍 Key Insights

• Data-Driven Approach: Demonstrates the application of unsupervised learning for personalized recommendation systems.
• Effective Clustering: Clustering simplifies the recommendation process by narrowing down candidates.
• Scalability: Ready for integration with large-scale datasets or additional features.
• Interactive Visualizations: Improves transparency and interpretability of the model’s results.

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🎯 Future Enhancements

Real-Time Data Integration:

• Add more metadata or integrate with live Spotify API data in the future.

Advanced Models:

• Explore deep learning techniques such as Autoencoders for feature extraction.

Enhanced Clustering:

• Experiment with algorithms like DBSCAN or Hierarchical Clustering for better performance on sparse datasets.

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💡 Contributions

Data enthusiasts and developers are welcome! Feel free to fork the repository and submit pull requests to enhance the project.

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