Heart Disease Prediction

Combination of machine learning classification models to predict whether a person has a heart disease

Table of contents

• Description
• Methodologies
• Results
• Files
• Technologies
• Packages
• Setup
• Usage
• Status
• Inspiration
• Contact

Description

The objective of this project is to predict

Data source: framingham_heart_disease.csv - Kaggle

Methodologies

This project was created as the capstone assignment for Applied Machine Learning course at UT Dallas, including 2 projects:

Project 1 has 2 parts:

• Part 1: Regression Task - Bike Rental Prediction: data cleaning, supervised machine learning regression models
• Part 2: Classification Task - Heart Disease Prediction: data cleaning, supervised machine learning classification models
  • a mix of classification models (ensemble learning, classification machine learning, principal component analysis and deep learning) to provide hospitals a filtering system that helps patients identify and prevent potential predictors of having a heart disease
  • Perform data cleansing and exploratory data analysis

Project 2 has 2 parts:

• Part 1: Regression Task - Bike Rental Prediction: ensemble learning, principal component analysis (PCA), regression models using PCA, deep learning (DL) models using neural networks
• Part 2: Classification Task - Heart Disease Prediction: ensemble learning, principal component analysis (PCA), classification models using PCA, deep learning (DL) models using neural networks
  • Apply a combination of models including Emsemble Learning (voting classifiers, bagging, pasting, adaboost and gradient boosting), Principal Component Analysis, Classification Machine Learning (KNN, Logistic Regression, Linear Support Vector Machine, Kernelilzed Support Vector Machine (rbf, poly, and linear), Decision Tree) and Deep Learning to predict whether a patient has a heart disease
  • Create Tableau dashboards for data analysis and visualization
  • Automate the modeling process and deploy the chosen model into production in Azure ML

Results

• Model Result: ARIMA time series provided the highest accuracy with a MSE of 0.22 (followed by KNN).
• Visualization: Tableau dashboards for the stock portfolio analysis were created here.
• Automated ML and Deployment: the chosen model (time series) was applied in Azure ML and deployed into production here.

Files

For all models, Close price is the predicted value.

• requirements.txt: text file containing all Python libraries used in the project

Technologies

Project is created with:

• MS Excel
• SQL
• MySQL Server
• Anaconda
• Jupyter Lab
• Jupyter Notebook
• Google Colab
• Python 3
• R Studio
• Tableau
• Azure ML Studio
• Windows

Packages

• pandas
• numpy
• matplotlib
• seaborn
• tensorflow
• keras
• beautifulsoup
• requests
• scikit-learn
• statsmodels.tsa
• mysql.connector

Setup

• Download all data files
• Install the requirements using pip install -r requirements.txt.
  • Make sure you use Python 3

Usage

• Run Stock Price Prediction.ipynb to see all project steps

Status

Project is finished.

Inspiration

Project inspired by Analytics Vidhya's tutorials of prediction models.

Contact

Created by @mypham14 - feel free to contact me on my LinkedIn!