Machine Learning Project: Logistic Regression vs Naïve Bayes

This repository contains a MATLAB-based machine learning project comparing Logistic Regression and Naïve Bayes classifiers using the Cleveland Heart Disease dataset from the UCI Machine Learning Repository.

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Project Overview

• Implementation of Logistic Regression and Naïve Bayes in MATLAB
• Performance comparison between the two classifiers
• Analysis based on classification accuracy and results visualization

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Dataset

The dataset used in this project is the Cleveland Heart Disease dataset from the UCI repository.

Source:
https://archive.ics.uci.edu/dataset/45/heart+disease

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Repository Contents

• projectcode.m – MATLAB code for model implementation and comparison
• processed.cleveland.data – Dataset used for the analysis
• Comparison of Naïve Bayes and Logistic Regression.pdf – Project report
• Graph Pictures/ – Output graphs and visualizations
• Other Codes/ – Supporting MATLAB scripts

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Technologies Used

• MATLAB

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Notes

This project was created for academic purposes in a coursework presentation to understand and compare classical machine learning classification algorithms.