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This GitHub repository hosts a simple machine learning model for music genre classification, employing the DecisionTreeClassifier. The model is trained on a CSV-formatted music dataset, offering an easy-to-understand introduction to classification techniques.

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DecisionTreeClassifier

This is my very first GitHub repository and it hosts a simple machine learning model for music genre classification, employing the DecisionTreeClassifier. The model is trained on a CSV-formatted music dataset, offering an easy-to-understand introduction to classification techniques.

#Features: Algorithm: DecisionTreeClassifier

Dataset: CSV-formatted music dataset

Inspiration: Derived from a YouTube tutorial @ Programming with Mosh

Usage: Quickly train and predict music genres using the provided code

Overview: The DecisionTreeClassifier is a classification algorithm that builds a tree-like structure based on input features, making it ideal for interpreting decision-making processes. In this project, it's applied to a music dataset to showcase its effectiveness in genre classification.

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This GitHub repository hosts a simple machine learning model for music genre classification, employing the DecisionTreeClassifier. The model is trained on a CSV-formatted music dataset, offering an easy-to-understand introduction to classification techniques.

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