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https://github.com/hasanfadhlillah/spotify-music-recommendation-system

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.
https://github.com/hasanfadhlillah/spotify-music-recommendation-system

ai clustering cosinesimilarity cosinesimilarity-pca-randomforest crossplatform dataanalysis datavisualization kmeans machinelearning musicrecommendation musictech pca python randomforest recommendersystem spotify tiktok youtube

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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.

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README 

        
# 🎵 SoundMatch: Spotify Music Recommendation System



![Spotify](https://img.shields.io/badge/Spotify-1ED760?style=for-the-badge&logo=spotify&logoColor=white)

![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)

![Pandas](https://img.shields.io/badge/pandas-%23150458.svg?style=for-the-badge&logo=pandas&logoColor=white)

![scikit-learn](https://img.shields.io/badge/scikit--learn-%23F7931E.svg?style=for-the-badge&logo=scikit-learn&logoColor=white)

![NumPy](https://img.shields.io/badge/numpy-%23013243.svg?style=for-the-badge&logo=numpy&logoColor=white)



## 🎧 About The Project



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.



### 📋 Project Overview



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



### 📊 Dataset



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



### 🎯 Key Features



- **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



## 🎼 Project Goals



1. Create an accurate and personalized music recommendation system

2. Analyze cross-platform music engagement patterns

3. Identify key factors influencing song popularity

4. Provide insights into current music trends

5. Enhance user music discovery experience



## 📊 Key Insights



- **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



## 🛠 Technical Implementation



### Data Processing

- Comprehensive data cleaning and preprocessing

- Feature engineering for enhanced accuracy

- Missing value handling with advanced imputation techniques



### Algorithms Used

- K-Means Clustering for song grouping

- Principal Component Analysis (PCA) for dimensionality reduction

- Cosine Similarity for recommendation generation

- Random Forest Classifier for cluster prediction



## 📈 Results and Performance



- 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



## 🎵 Features in Detail



1. **Engagement Score Calculation**

   - Spotify Popularity (30%)

   - Playlist Count (25%)

   - YouTube Views (20%)

   - TikTok Views (15%)

   - TikTok Posts (10%)



2. **Cross-Platform Analysis**

   - Spotify metrics

   - YouTube engagement

   - TikTok virality

   - Platform-specific trends



## 🔍 Conclusions



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



## 🚀 Future Improvements



- Real-time data integration

- Enhanced user preference learning

- Additional platform integration

- Advanced visualization features

- API development for third-party integration



## 👥 Contributors



- Mutiara Shabrina

- Muhammad Hasan Fadhlillah



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