📌 Project Overview

This project focuses on predicting the popularity of songs using Machine Learning techniques. By analyzing various audio and metadata features, the model attempts to determine whether a song is likely to be popular. The project is implemented primarily using Python and Jupyter Notebooks, making it easy to experiment with data, models, and visualizations.

The goal of this project is to demonstrate a complete ML workflow — from data preprocessing and exploratory data analysis (EDA) to model training, evaluation, and result interpretation.

---

🚀 Features

• Data preprocessing and cleaning
• Exploratory Data Analysis (EDA)
• Feature selection and transformation
• Machine Learning model training
• Model evaluation and performance comparison
• Visual insights for better understanding of data and predictions

---

🧠 Machine Learning Concepts Used

• Supervised Learning
• Classification
• Feature Engineering
• Train-Test Split
• Model Evaluation Metrics (Accuracy, Precision, Recall, etc.)

---

🛠️ Tech Stack

• Programming Language: Python

• Environment: Jupyter Notebook

• Libraries Used:

  • NumPy
  • Pandas
  • Matplotlib
  • Seaborn
  • Scikit-learn

---

📂 Project Structure

Music-Prediction/
│
├── musicPredictionML.ipynb      # Main ML notebook
├── report.pdf                  # Project report
├── .gitignore
└── README.md

---

📊 Dataset

The dataset contains various audio-related attributes such as tempo, loudness, energy, danceability, and other musical features. These attributes are used as input features to train the prediction model.

Note: Dataset details and preprocessing steps are explained inside the notebook.

---

⚙️ How to Run the Project

1. Clone the repository:

   git clone https://github.com/your-username/your-repo-name.git

2. Navigate to the project directory:

   cd your-repo-name

3. Open Jupyter Notebook:

   jupyter notebook

4. Run musicPredictionML.ipynb step by step.

---

📈 Results

The trained model is able to predict song popularity with reasonable accuracy based on the selected features. Performance metrics and visualizations are included in the notebook for detailed analysis.

---

📌 Future Improvements

• Use deep learning models for better accuracy
• Add more features such as lyrics analysis
• Deploy the model as a web application
• Use real-time data from music streaming APIs

---

🤝 Contributing

Contributions are welcome! Feel free to fork the repository, create a new branch, and submit a pull request.

---

📜 License

This project is open-source and available under the MIT License.

---

👤 Author

Prateek Singh B.Tech Student | Machine Learning Enthusiast

⭐ If you found this project helpful, consider giving it a star on GitHub!