This repository contains a music categorization tool that utilizes the music_genre.csv dataset for training and evaluation. The tool applies various machine learning algorithms to process the data and classify music into different genres.
The dataset used for this project is music_genre.csv. It contains a collection of music samples with corresponding genre labels. Each sample is represented by a set of features, such as tempo, energy, and danceability, which are used to train the machine learning models.
The music categorization tool employs several machine learning algorithms to process the dataset and classify the music samples. The following algorithms are implemented and evaluated:
- Decision Tree Classifier
- Liner Regresion
- Support Vector Machine (SVM)
- K-Nearest Neighbors (KNN)
- ANN
The categorization tool generates evaluation metrics, such as accuracy, precision, recall, and F1-score, for each machine learning algorithm. These metrics provide insights into the performance of each algorithm in classifying the music samples into their respective genres.
Contributions are welcome! If you would like to contribute to this project, please fork the repository and create a pull request with your proposed changes. Remember to follow the existing coding style and add appropriate tests for new functionality.
This project is licensed under the MIT License. Feel free to use and modify the code as per the license terms.
Please refer to the individual files in the repository for more details and specific licensing information.
Special thanks to the creators and maintainers of the music_genre.csv dataset, as well as the contributors to the machine learning algorithms implemented in this project. Their work has made this tool possible.
If you have any questions or suggestions, please feel free to contact us at ismailtosuntnyl@gmail.com. We appreciate your feedback!