🎵 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.joblibrandom_forest.joblibxgboost.joblibsvm.joblibvoting_classifier.joblibcatboost_model.cbmann_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.
