Initial Release of Heart Disease Classification Project

This is the initial release of the Heart Disease Classification project. In this version, the following features and models are included:

• Implemented Models:

  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Gaussian Naive Bayes
  • Support Vector Machine (SVM)
  • Decision Tree
  • Random Forest
  • AdaBoost
  • Gradient Boosting
  • XGBoost

• Key Features:

  • Hyperparameter tuning using grid search for optimal performance.
  • Cross-validation to evaluate model robustness and avoid overfitting.
  • Comparison of models based on cross-validation and test accuracy.

• Best Model:

  • XGBoost achieved the highest test accuracy of 64.13%.

This release includes all the necessary scripts for training, testing, and evaluating the models, as well as an example dataset for easy setup and testing.

Changelog:

• Initial implementation of machine learning models.
• Dataset preprocessing and feature standardization.
• Hyperparameter tuning for each model.
• Evaluation metrics included: accuracy on cross-validation and test set.