This Jupyter Notebook is a comprehensive analysis tool for classifying and exploring various music genres using the Spotify API. It employs a combination of data collection, preprocessing, and machine learning techniques to build a robust model that can classify songs into genres such as pop, rock, jazz, classical, and hip-hop.
Update the credentials.txt with your own Spotify API credentials and launch the notebook.
Uses the spotipy library to authenticate and fetch music tracks along with their metadata from Spotify, covering multiple genres.
Scripted processes to collect extensive data on tracks, including artist names, track IDs, and audio features like danceability, energy, and tempo.
Implements techniques to clean and standardize the data, preparing it for effective machine learning.
Enhances the dataset with calculated features and transformations to improve model accuracy.
Utilizes several machine learning algorithms (e.g., Logistic Regression, Random Forest, and SVM) to classify tracks into genres. Includes cross-validation and hyperparameter tuning to optimize model performance.
- Music recommendation systems.
- Audio feature analysis for academic research.
- Enhancing metadata for large music databases.
- Python for scripting.
- Pandas and NumPy for data manipulation.
- Scikit-learn for machine learning.
- Spotipy for interfacing with Spotify Web API.
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Handling authentication and data retrieval from Spotify.
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Managing large datasets and ensuring data quality.
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Building and tuning classifiers to accurately predict music genres based on audio features.
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PS: Analysis Notebook is only the analysis of my own dataset retrieved through the API.Each credential give unique dataset and thus unique results.