CS-433 Machine Learning - Project 1

This project is part of the EPFL CS-433 Machine Learning course, providing students with a hands-on opportunity to work through the entire machine learning process on a real-world dataset.

Table of Contents

• About
• Getting Started
• Usage
• Contributing

About

The goal of the project is to apply machine learning techniques to determine the risk of a person in developing CVD based on features of their personal lifestyle factors. The data comes from the Behavioral Risk Factor Surveillance System (BRFSS). Respondents were classified as having coronary heart disease (MICHD) if they reported having been told by a provider they had MICHD or had a heart attack.

Getting Started

Navigate to the run directory to access and run the code. Please refer to the list below for an overview of each files' functionality:

• implementations.py: Defines machine learning models used for training.
• helpers.py: Includes functions for loading and exporting data, its manipulation and exploration.
• data_preprocessing.py: Includes functions for data cleaning, preprocessing, feature selection.
• plot.py: Contains functions for visualization. Ensure that the data required for run.ipynb is placed in a folder named data within the same repository run - where the run.ipynb file is located. The data folder should contain the following CSV files: x_train.csv, x_test.csv, y_train.csv, train_ids.csv, and test_ids.csv.

Prerequisites

This project requires Python 3.8 and the following libraries:

• numpy
• seaborn
• matplotlib

Usage

1. Navigate to the run directory and ensure that the data is placed in a folder named data within the same repository - where the run.ipynb file is located.
2. Run the run.ipynb file.

Acknowledgments

• Machine Learning Project 1 GitHub repo.
• This work results from the collaboration of Ginevra Larroux and Antoine Vincent, EPFL Master's students in Energy Science and Technology.