---
license: mit
tags:
  - recommendation
  - binary-classification
  - music
  - ensemble
---

# 🎵 Music Recommendation System

This repository hosts a collection of machine learning models designed to recommend songs by predicting whether a user is likely to "like" a track based on its audio features.

## 📁 Files Included

- `data.csv` — Dataset of 195 songs with features like danceability, energy, loudness, tempo, etc.
- Trained model files:
  - `logistic_regression.joblib`
  - `random_forest.joblib`
  - `xgboost.joblib`
  - `svm.joblib`
  - `voting_classifier.joblib`
  - `catboost_model.cbm`
  - `ann_model.keras`
- `final_model_card_scaled.pdf` — Full model evaluation, comparison table, and chart

## 🧠 Models Used

- Logistic Regression
- Random Forest
- XGBoost
- Support Vector Machine (SVM)
- Voting Classifier (Ensemble)
- CatBoost
- Artificial Neural Network (ANN)

## 📊 Evaluation

All models were evaluated using:
- Accuracy
- Precision
- Recall
- F1-Score

Refer to the PDF `final_model_card_scaled.pdf` for full details.

## 📬 Contact

Maintained by Sujal Thakkar.