This project aims to predict the risk of coronary heart disease (MICHD) using various machine learning models. Utilizing data from the Behavioral Risk Factor Surveillance System (BRFSS) survey from 2015, we focus on lifestyle and clinical factors to implement binary classification techniques like gradient descent or ligistic regression. The outcome is intended to support public health initiatives in promoting healthier lifestyle choices and facilitating early interventions to combat heart-related diseases.
- Python 3.8+
- Libraries: numpy, matplotlib
- Clone the Repository: Clone this repository to your local machine using the following command: git clone https://github.com/lucilemln/ML-project.git
- Download the folder data : The project folder need to be one level above the project folder and name data
In this project, we have implemented and assessed the performance of the following machine learning models:
- Gradient Descent with Mean Squared Error
- Stochastic Gradient Descent with Mean Squared Error
- Least Squares Regression
- Ridge Regression
- Logistic Regression
- Regularized Logistic Regression