tarakakoda / music-genre-prediction-with-decision-tree-ml-in-python

Design a Music Genre Recommendation System in Python Using a Decision Tree Classifier. This code contains training, testing, prediction, and model storage in Jupyter Notebook. Begin your machine learning career with this repo for Decision Tree music genre classification.

Music-Genre-Prediction-with-Decision-Tree-ML-in-Python JupyterNotebook

Design a Music Genre Recommendation System in Python Using a Decision Tree Classifier. This code contains training, testing, prediction, and model storage in Jupyter Notebook. Begin your machine learning career with this repo for Decision Tree music genre classification.

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Machine learning

Machine learning is a powerful method for handling complicated image classification issues, such as determining whether an image depicts a cat, dog, or horse. The primary benefit of employing machine learning is that it can learn from vast volumes of labelled data to uncover patterns and characteristics that differentiate between various classes. This reduces the need for manual rule-based programming and increases system flexibility and adaptability. The model's accuracy in identifying fresh photos may be constantly improved with the addition of new data, guaranteeing that it can keep up with changing real-world situations. As a result, machine learning may be used to create strong and accurate picture categorization systems.

Applications

• Self-driving cars
• Robotics
• Language Processing
• Vision Processing
• Forecasting Stock Market Trends
• Customer Behavior Analysis
• Fraud Detection and more

Machine Learning in Action

Machine learning projects typically involve several key steps :

• Import the Data
• Clean the Data
• Split the Data into Training/Test Sets
• Create a Model
• Train the Model
• Make Predictions
• Evaluate and Improve

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Getting Started with Machine Learning : Music Genre Prediction

These codes can be used as a reference to build a music genre classifier using the Decision Tree algorithm in scikit-learn library. The codes includes the implementation of the model training, testing and prediction, as well as the storage of the trained model in Jupyter Notebook. This repository can serve as a starting point for those interested in exploring Decision Trees for music genre classification and can be easily modified to fit specific needs and data.

Requirement :

• Python 3.x
• pandas library for data manipulation.
• scikit-learn library for machine learning algorithms.
• graphviz library for visualizing decision trees.
• Jupyter Notebook for executing code Installing Jupyter Notebooks/Anaconda
• scikit-learn's joblib library for storing trained models for future use.
• A dataset for training the Decision Tree classifier music.csv

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If you have any ideas or suggestions for improving this project, feel free to fork the repository, make changes, and submit a pull request. With your contributions, we can make this project even better.