CS4641 Group 5 Machine Learning Project

Project Overview

This project aims to develop machine learning models for analyzing and predicting heart-related health outcomes. The project is structured into various directories for data preprocessing, analysis, and implementation of both supervised and unsupervised learning algorithms.

Directory Structure

• /Data/: Contains raw and preprocessed datasets.
• /DataAnalysis/: Scripts and notebooks for exploratory data analysis.
• /GitHub_Pages/: Resources for the project's GitHub Pages site.
  • GitHub_Pages/Images/: Images used for the GitHub Pages site, such as plots, graphs, and code snapshots
• /Preprocessing/: Scripts for data cleaning and preprocessing.
  • /Preprocessing/PCA.py: Script implementing PCA for dimensionality reduction
  • /Preprocessing/cleaning.py: Script for cleaning missing values from the dataset
• /SupervisedLearning/: Implementation of supervised learning algorithms.
  • /SupervisedLearning/randomforest.py: Script implementing the random forest model using provided dataset
  • /SupervisedLearning/randomforest_cleaned.ipynb: Jupyter notebook containing code for random forest model and visualizations and quantitative metrics representing model output
  • /SupervisedLearning/KNN_jupyter.ipynb: Jupyter notebook containing code for implemented K-Nearest Neighbors model, visualizations and plotting, and quantitative metrics representing model output
  • /SupervisedLearning/NeuralNetworkModified.ipynb: Jupyter notebook containing code for implemented Neural Network model, visualizations and plotting, and quantitative metrics representing model output
• /index.md: GitHub Page detailing all progress of the project in the form of a report

Getting Started

Prerequisites

• Python 3.10

Installation

1. Clone the repository:

   git clone https://github.com/sunnypark12/cs4641_group5.git
   cd cs4641_group5
   

Usage

Data Preprocessing Navigate to the Preprocessing directory and run the preprocessing scripts to clean and prepare the data.

Data Analysis Explore the data using the notebooks and scripts in the DataAnalysis directory.

Model Training Train and evaluate models using the scripts in the SupervisedLearning directory.