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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.
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