https://github.com/afolabi022/music-recomendation-system
🎵 Music Recommendation System . This project is a machine learning-powered Music Recommendation System built and deployed using Streamlit. The system provides personalized music recommendations by clustering songs based on user preferences.
https://github.com/afolabi022/music-recomendation-system
euclidean-algorithm euclidean-distances kmeans-clustering machine-learning music music-bot music-recommendation-system recommendation-system recommender-system
Last synced: 3 months ago
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🎵 Music Recommendation System . This project is a machine learning-powered Music Recommendation System built and deployed using Streamlit. The system provides personalized music recommendations by clustering songs based on user preferences.
- Host: GitHub
- URL: https://github.com/afolabi022/music-recomendation-system
- Owner: afolabi022
- Created: 2024-11-24T09:17:12.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-11-28T23:34:22.000Z (7 months ago)
- Last Synced: 2025-02-09T11:12:56.159Z (4 months ago)
- Topics: euclidean-algorithm, euclidean-distances, kmeans-clustering, machine-learning, music, music-bot, music-recommendation-system, recommendation-system, recommender-system
- Language: Jupyter Notebook
- Homepage: https://music-recomendation-system-7zukmujcfsxxtappgzo9b3a.streamlit.app/
- Size: 116 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🎵 Music Recommendation System
This project is a machine learning-powered **Music Recommendation System** built and deployed using **Streamlit**. The system provides personalized music recommendations by clustering songs based on user preferences. It leverages the **K-Means clustering algorithm** and calculates similarity using **Euclidean distance**.
You can access the live, deployed app here:
👉 [Music Recommendation System - Streamlit App](https://music-recomendation-system-7zukmujcfsxxtappgzo9b3a.streamlit.app/)
---
## 🌟 Features
- **Data Transformation**: Converts unstructured data into structured data for analysis.
- **Interactive User Interface**: An easy-to-use interface built with Streamlit.
- **Music Clustering**: Groups songs into clusters based on features like genre, tempo, and other metadata.
- **Personalized Recommendations**: Suggests songs similar to a user's selected track.
- **Efficient Similarity Calculation**: Uses Euclidean distance for finding song similarities.
---
## 🚀 How It Works
1. **Data Collection**:
- Music data was obtained from Dropbox as unstructured data.
- The data was preprocessed and converted into a structured format (e.g., CSV).
2. **Feature Engineering**:
- Extracted relevant features from the dataset, such as tempo, genre, and pitch-related attributes.
3. **K-Means Clustering**:
- Grouped songs into clusters based on their attributes.
- Each cluster represents songs with similar characteristics.
4. **Recommendation Engine**:
- When a user selects a song, the app identifies its cluster.
- Recommendations are made from the same cluster using Euclidean distance for similarity ranking.
---
## 📦 Installation and Setup
Follow these steps to run the app locally:
1. **Clone the Repository**:
```bash
git clone https://github.com/your-username/music-recommendation-system.git
cd music-recommendation-system
```
2. **Install Dependencies**:
Create a virtual environment and install the required packages.
```bash
python -m venv env
source env/bin/activate # Use `env\Scripts\activate` on Windows
pip install -r requirements.txt
```
3. **Run the App**:
```bash
streamlit run app.py
```
4. **Access the App**:
Open your browser and navigate to `http://localhost:8501`.
---
## 🛠 Tools and Technologies
- **Programming Language**: Python
- **Framework**: Streamlit
- **Algorithms**: K-Means Clustering, Euclidean Distance
- **Data Preprocessing**: Conversion of unstructured data into structured formats.
- **Libraries**:
- `numpy`
- `pandas`
- `scikit-learn`
- `librosa` (if working with audio features)
- `matplotlib` (for visualizations)
---
## 📂 Project Structure
```
music-recommendation-system/
│
├── app.py # Streamlit app code
├── data/
│ └── songs_data.csv # Preprocessed structured dataset
├── models/
│ └── kmeans_model.pkl # Pre-trained K-Means model (if applicable)
├── requirements.txt # Dependencies
└── README.md # Project documentation
```
---
## ✨ Key Highlights
- **Data Transformation**: Preprocessed unstructured music data into a structured dataset suitable for machine learning.
- **Unsupervised Learning**: Demonstrates clustering for recommendation systems.
- **Customizable Recommendations**: Flexible system that can adapt to new datasets or features.
- **Scalable Deployment**: Deployed using Streamlit for easy accessibility and scalability.
---
## 🖼 Demo
*A screenshot or GIF of the app in action.*
---
## 🧑💻 Author
Developed by **[Afolabi Azeez](https://github.com/afolabi022)**.
Feel free to connect with me for feedback or collaboration.
---
## 📜 License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
---