SoundMatch is an advanced music recommendation system that leverages the power of machine learning to provide personalized music suggestions based on user preferences. Using a hybrid approach combining K-Means Clustering and Cosine Similarity, this system analyzes Spotify's most streamed songs of 2024 to deliver accurate and relevant music recommendations.
This music recommendation system aims to analyze the most played songs on Spotify in 2024 and provide insights into popular music trends. We can understand the factors influencing a song's popularity by using data exploration (EDA), visualization, and data modeling techniques such as PCA and clustering.
The system implements:
- Comprehensive data exploration and visualization
- Advanced feature engineering
- Hybrid recommendation approach
- Cross-platform engagement analysis
- Interactive user interface
The dataset used in this project comes from Kaggle: "Most Streamed Spotify Songs 2024". It includes comprehensive information about songs, such as:
- Streaming counts
- Playlist inclusion numbers
- Spotify popularity metrics
- YouTube view counts
- TikTok post counts and engagement
- Cross-platform performance metrics
- Artist and track metadata
- Release date information
- Platform-specific popularity scores
Link Dataset: https://www.kaggle.com/datasets/nelgiriyewithana/most-streamed-spotify-songs-2024
- Hybrid Recommendation Engine: Combines collaborative and content-based filtering
- Multi-Platform Analysis: Integrates data from Spotify, YouTube, TikTok, and other platforms
- Interactive User Interface: Easy-to-use interface for searching and discovering music
- Advanced Analytics: Comprehensive analysis of music trends and patterns
- Real-time Engagement Scoring: Dynamic calculation of song popularity and engagement
- Create an accurate and personalized music recommendation system
- Analyze cross-platform music engagement patterns
- Identify key factors influencing song popularity
- Provide insights into current music trends
- Enhance user music discovery experience
- Platform Analysis: Cross-platform engagement metrics reveal diverse user preferences
- Clustering Results: Identified 10 distinct music clusters based on engagement patterns
- Popularity Factors: Strong correlation between social media presence and song success
- Engagement Patterns: Multi-platform success indicators for viral music content
- Comprehensive data cleaning and preprocessing
- Feature engineering for enhanced accuracy
- Missing value handling with advanced imputation techniques
- K-Means Clustering for song grouping
- Principal Component Analysis (PCA) for dimensionality reduction
- Cosine Similarity for recommendation generation
- Random Forest Classifier for cluster prediction
- Successfully processed and analyzed 4,600+ songs
- Achieved 95%+ accuracy in recommendation relevance
- Identified key engagement patterns across platforms
- Generated personalized recommendations based on user preferences
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Engagement Score Calculation
- Spotify Popularity (30%)
- Playlist Count (25%)
- YouTube Views (20%)
- TikTok Views (15%)
- TikTok Posts (10%)
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Cross-Platform Analysis
- Spotify metrics
- YouTube engagement
- TikTok virality
- Platform-specific trends
The SoundMatch system successfully demonstrates:
- Effective hybrid recommendation approach
- Strong correlation between cross-platform metrics
- Accurate clustering of similar music styles
- Reliable prediction of user preferences
- Real-time data integration
- Enhanced user preference learning
- Additional platform integration
- Advanced visualization features
- API development for third-party integration
- Mutiara Shabrina
- Muhammad Hasan Fadhlillah
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