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- license: cc-by-nc-4.0
 
 
 
 
 
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+ license: mit
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+ tags:
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+ - recommendation
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+ - binary-classification
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+ - music
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+ - ensemble
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+
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+ # 🎡 Music Recommendation System
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+
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+ 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.
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+
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+ ## πŸ“ Files Included
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+
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+ - `data.csv` β€” Dataset of 195 songs with features like danceability, energy, loudness, tempo, etc.
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+ - Trained model files:
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+ - `logistic_regression.joblib`
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+ - `random_forest.joblib`
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+ - `xgboost.joblib`
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+ - `svm.joblib`
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+ - `voting_classifier.joblib`
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+ - `catboost_model.cbm`
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+ - `ann_model.keras`
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+ - `final_model_card_scaled.pdf` β€” Full model evaluation, comparison table, and chart
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+
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+ ## 🧠 Models Used
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+
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+ - Logistic Regression
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+ - Random Forest
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+ - XGBoost
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+ - Support Vector Machine (SVM)
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+ - Voting Classifier (Ensemble)
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+ - CatBoost
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+ - Artificial Neural Network (ANN)
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+
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+ ## πŸ“Š Evaluation
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+
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+ All models were evaluated using:
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+ - Accuracy
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+ - Precision
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+ - Recall
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+ - F1-Score
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+
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+ Refer to the PDF `final_model_card_scaled.pdf` for full details.
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+
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+ ## πŸ“¬ Contact
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+
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+ Maintained by Sujal Thakkar.