🎵Music Emotion Classification with Machine Learning

Affective Computing on Audio Features using ML Models

🚀 A machine learning project to classify emotions in music based on Spotify audio features.

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📌 Project Overview

This project aims to predict the emotion of a song based on its audio features extracted from Spotify and other datasets. Using machine learning models, we classify each song into emotions such as happy, sad, fear, angry, surprise, etc.

Key steps in this project:

1. Data Collection & Cleaning → Extracting and merging music features & emotion labels.
2. Exploratory Data Analysis (EDA) → Understanding feature distributions & correlations.
3. Machine Learning Models → Training & evaluating models like KNN, Logistic Regression, and Random Forest.
4. Feature Importance Analysis → Identifying the most impactful audio features for emotion prediction.
5. Binary Classification for Feature Evaluation → Testing individual feature impact in classification.

📂 Project Structure

📁 emotion-music-ml/
│── 📄 README.md                 # Project documentation
│── 📄 .gitignore                # Files to ignore in Git
│── 📁 notebooks/                # Jupyter notebooks for different stages
│   ├── DataCollection&Wrangling.ipynb           # Data collection & Wrangling
│   ├── Preprocessing&MachineLearning.ipynb      # Data Preprocessing & Machine Learning Models
│── 📁 results/                   # Model performance reports & graphs
│   ├── Decoding Emotion in Music_MACS30100 Project.pdf

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📊 Datasets Used

1️⃣ Music Features Dataset

• Source: (Spotify Audio Features dataset)[https://www.kaggle.com/datasets/maltegrosse/8-m-spotify-tracks-genre-audio-features]
• Content: Danceability, energy, loudness, valence, tempo, and other musical properties. 2️⃣ Emotion Labels
• Source: (Last.fm Million Songs)[http://millionsongdataset.com/lastfm/]
• Content: Songs labeled with emotions such as happy, sad, angry, calm, energetic, etc. 3️⃣ Processed Dataset
• merged_emotion_audio_features.csv → Final dataset after merging all features & labels.

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🛠️ Installation & Setup

1️⃣ Clone the Repository git clone https://github.com/YOUR_GITHUB_USERNAME/emotion-music-ml.git cd emotion-music-ml

2️⃣ Create a Virtual Environment python -m venv env source env/bin/activate # On macOS/Linux env\Scripts\activate # On Windows

3️⃣ Install Dependencies pip install -r requirements.txt

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