Datagotchi Health

Datagotchi Health is an algorithm designed to predict mental health outcomes based on lifestyle behaviors and provide recommendations for improving mental health.

Table of Contents

1. Prerequisites / Installation
2. Getting Started
3. Repository Structure

Prerequisites / Installation

Before you begin, ensure you have met the following requirements:

• Python 3.9
• R
• Poetry (for Python virtual environment)
• Make (to run the Makefile)

To install the required dependencies, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/datagotchi-health.git

2. Navigate to the project directory:

   cd datagotchi-health

3. Install the dependencies using Poetry:

   poetry install

Getting Started

To get started with the project, follow these steps:

1. Request access to the data by contacting the repository owner.
2. Create a .env file in the project root directory with the DATA_PATH environment variable set to the location of your data:

   DATA_PATH='path/to/data'

3. Edit the config.py file to suit your specific needs.
4. Run the ML pipeline :

• Step 1 : create features :

  make create-features

• Step 2 : select features :

  make select-features

• Step 3 : Run the cross-validation:

  make run-crossval

Repository Structure

The repository is organized as follows: code

code/
├── cleaning/
│   └── (various cleaning functions)
├── eda/
│   └── (exploratory data analysis scripts)
└── ml/
    └── (machine learning workflow from raw data to evaluated predictions)

• cleaning: Contains scripts for data cleaning.
• eda: Contains scripts for exploratory data analysis to explore and understand the data.
• ml: Contains scripts for the machine learning workflow, including data processing, model training, and evaluation.