nmcintyre5 / heartdiseaseprediction

This Pyspark script uses a supervised machine learning to predict heart disease using Apache Spark's PySpark library.

Heart Disease Prediction using PySpark

This Pyspark script uses a supervised machine learning to predict heart disease using Apache Spark's PySpark library. The model is trained on a dataset containing various factors such as biking, smoking, and other attributes related to heart health.

Overview

Heart disease is a significant health concern worldwide. This project aims to predict the likelihood of heart disease based on demographic and lifestyle factors. The predictive model is implemented using a linear regression model and leverages the distributed computing capabilities of Apache Spark for scalable data processing.

Key Features

• Utilizes PySpark's distributed computing for efficient data processing and model training.
• Implements linear regression modeling to predict the probability of heart disease.
• Conducts correlation analysis to identify relevant features.
• Evaluates model performance using root mean squared error (RMSE) and R-squared metrics.

How to Install

1. Clone the repository:

   git clone https://github.com/nmcintyre5/heartDiseasePrediction.git

2. Navigate to the project directory:

   cd heartDiseasePrediction
   

3. Install dependencies:

   pip install pyspark scikit-learn kaggle
   

Credits

This project is based on a Coursera project.