Heart Disease Classification Project

This project implements various machine learning classification approaches to predict heart disease using a heart disease dataset.

Project Overview

The project implements and compares five different classification approaches:

1. Logistic Regression
2. Decision Tree
3. Random Forest
4. Stochastic Gradient Descent (SGD)
5. Support Vector Machine (SVM)

Key Features

• Dataset preprocessing and cleaning
• Implementation of multiple classification algorithms
• Hyperparameter tuning using GridSearchCV
• Feature importance analysis and feature selection experiments
• Model performance comparison and evaluation

Project Structure

• heart.csv: The dataset used for analysis
• EDA_Data_preeprocessing.ipynb: Jupyter notebook containing all analysis and model implementations
• pyproject.toml: Project dependencies and configuration

Requirements

• Python 3.x
• Required packages are listed in pyproject.toml

Analysis Steps

1. Dataset description and feature explanation
2. Data preprocessing and cleaning
3. Model implementation and training
4. Hyperparameter tuning using GridSearchCV
5. Feature selection analysis and model re-evaluation