Heart Disease Naive Bayes & Logistic Regression
This project implements and compares multiple classification algorithms for heart disease prediction: Naive Bayes variants (Gaussian, Categorical, and Mixed) and Logistic Regression. The project demonstrates different approaches to handling mixed data types (numeric, categorical, ordinal, binary) and comprehensive hyperparameter tuning using GridSearchCV. Each model is evaluated on the Heart Disease UCI dataset with proper preprocessing pipelines and cross-validation. The project showcases model-specific preprocessing strategies, solver-penalty compatibility handling, and comprehensive evaluation metrics.
Overview
This project implements and compares multiple classification algorithms for heart disease prediction: Naive Bayes variants (Gaussian, Categorical, and Mixed) and Logistic Regression. The project demonstrates different approaches to handling mixed data types (numeric, categorical, ordinal, binary) and comprehensive hyperparameter tuning using GridSearchCV. Each model is evaluated on the Heart Disease UCI dataset with proper preprocessing pipelines and cross-validation. The project showcases model-specific preprocessing strategies, solver-penalty compatibility handling, and comprehensive evaluation metrics.
Key Features
pages.portfolio.projects.heart_disease_naive_bayes_logistic.features.0
Logistic Regression with hyperparameter tuning
Model-specific preprocessing strategies
Comprehensive hyperparameter tuning with GridSearchCV
Cross-validation for robust evaluation
Solver-penalty compatibility handling
Full evaluation metrics suite
Mixed data type handling (numeric, categorical, ordinal, binary)
pages.portfolio.projects.heart_disease_naive_bayes_logistic.features.8
Technical Highlights
Compared multiple Naive Bayes variants and Logistic Regression on heart disease data
Implemented model-specific preprocessing for optimal performance
Handled solver-penalty compatibility issues in Logistic Regression
Achieved strong performance with tuned models
Comprehensive evaluation with cross-validation
pages.portfolio.projects.heart_disease_naive_bayes_logistic.highlights.5
Challenges and Solutions
Mixed Data Types
Handled numeric, categorical, ordinal, and binary variables with appropriate preprocessing
Solver-Penalty Compatibility
Managed solver and penalty combinations in Logistic Regression for optimal performance
Model Comparison
Compared multiple algorithms with fair evaluation metrics and preprocessing
Technologies
ML Models
Tuning
Preprocessing
Pipeline
Data
Environment
Project Information
- Status
- Completed
- Year
- 2024
- Architecture
- ML experimentation with multiple classifiers and preprocessing strategies
- Category
- Data Science