Bragi 🎵🧠

Bragi is a NLP model designed to classify songs into moods and provide music recommendations based on the identified emotion. Named after the Norse god of music and poetry, Bragi brings smart, emotion-aware music suggestions to life.

💡 Project Overview

Bragi analyzes song features and classifies each track into a specific mood category.

🧰 Features

• 🎼 Mood Classification: Uses lyrics to classify mood.
• 🔍 Model Flexibility: Easily extensible to support additional mood categories or song attributes.
• 📈 Scalable Pipeline: Can be integrated into streaming apps or music libraries.

🧠 Model Workflow

1. Input: Song metadata (e.g., lyrics, audio features).

2. Processing:

   • Feature extraction using SQLite for structured data storage.
   • Mood classification using a trained ML model tensorflow.

📦 Tech Stack

• Python
• Libraries: pandas, numpy, scikit-learn, tensorflow (optional, for deep learning models), matplotlib (for visualizations).
• Data Sets: Kaggle.

Enhancements

• Audio Features:
  • Tempo
  • Key
  • Valence
  • Energy
  • Count of artists
  • Popularity score

🚀 Installation

1. Clone the repository:

   git clone https://github.com/GabeSantini/MASDAE_IE7500_Bragi.git
   cd MASDAE_IE7500_Bragi

2. Install the required dependencies:

   pip3 install -r requirements.txt

3. Run the application or scripts as needed.