This project focuses on predicting the genre of music using machine learning techniques, with a primary emphasis on leveraging the Scikit-Learn library. We have implemented and optimized multiple models, including k-nearest neighbors (KNN), decision tree, and random forest, to enhance the efficiency and accuracy of music genre classification.

• We have thoroughly examined and comprehended the structure and content of the music dataset.
• Identified the pertinent features and the target variable for genre classification.

• Employed essential preprocessing techniques to clean and prepare the music dataset for model training.
• Handled missing data, standardized features, and resolved any inconsistencies.

• Extracted meaningful features from the music data, transforming them into a format suitable for machine learning.
• Utilized techniques like feature scaling and one-hot encoding to prepare the data for modeling.

• Implemented various machine learning models, including:
  • K-Nearest Neighbors (KNN): Utilized the KNN algorithm to classify music genres based on feature similarity.
  • Decision Tree: Built decision tree models to make genre predictions by splitting the data into nodes.
  • Random Forest: Employed ensemble learning with random forest models to improve classification accuracy.

• Fine-tuned model hyperparameters to enhance predictive performance.
• Employed techniques like cross-validation and grid search to find the optimal model settings.

• Evaluated the models' performance using appropriate evaluation metrics such as accuracy, precision, recall, and F1 score.
• Conducted a comprehensive analysis of the results to assess the effectiveness of genre classification.