🎵 Transformer-Based Music Popularity Prediction

This repository contains a PyTorch-based implementation of a Transformer model to predict the popularity of Spotify songs based on audio features.

🚀 Overview

The model learns to predict the track_popularity score (0–100) using a set of numerical audio features extracted from the spotify_songs.csv dataset. It uses a Transformer encoder architecture — originally developed for NLP tasks — and adapts it for regression.

🧠 Model Architecture

• Transformer Encoder with:

  • Positional projection from 1D to d_model (default: 32)
  • Multi-head self-attention (nhead = 4)
  • Two encoder layers (num_layers = 2)

• Output layer flattens sequence and predicts popularity as a single scalar value.

📊 Features Used

The following features are used as input:

• danceability
• energy
• loudness
• speechiness
• acousticness
• instrumentalness
• liveness
• valence
• tempo

Target:

• track_popularity

🧪 Dataset

This project expects a CSV file named spotify_songs.csv with the columns listed above. Missing values are dropped during preprocessing.

Note: Update the CSV path in the script if needed.

🛠 Requirements

• Python 3.8+
• PyTorch
• scikit-learn
• pandas
• matplotlib

You can install dependencies using:

pip install torch scikit-learn pandas matplotlib

🧰 How It Works

1. Data Preprocessing

• Features are standardized using StandardScaler.
• Target is kept as raw values (0–100).
• Data is split into training and testing sets.
• Features are reshaped into sequence format for the Transformer.

2. Training

• The model is trained using Mean Squared Error (MSE) loss.
• Optimizer: Adam
• Number of epochs: 15

3. Evaluation

• After training, the model is evaluated on the test set.
• Final MSE loss is printed.
• A training loss chart is displayed.

📈 Results

A plot of training loss over epochs helps visualize the learning curve.

You can save the loss plot in your script using plt.savefig("loss_plot.png").

📂 File Structure

├── spotify_songs.csv       # Dataset file
├── transformer_regression.py  # Main training script
└── README.md               # This file

📌 Example Usage

To run the script:

python transformer_regression.py