# Specification for Implementing a Kaggle Competition Project

This document outlines the structure and interface protocols for implementing a machine learning project, similar to a Kaggle competition. Follow these guidelines to ensure consistency and maintainability across projects.

## Project Structure

The project should be organized into the following components:

1. **Data Loading** (`load_data.py`): A module responsible for loading and preprocessing raw data.
2. **Feature Engineering**(`feat*.py`): A module for transforming raw data into features suitable for model training.
3. **Model Workflow**(`model*.py`): A module that manages the training, validation, and testing of machine learning models.
4. **Ensemble and Decision Making**(`ensemble.py`): A module for combining predictions from multiple models and making final decisions.
5. **Workflow**(`main.py`): A script to put the above component together to get the final submission(`submission.csv`)

## Submission

- Implement a script to generate the submission file.
- The script should write predictions to a CSV file in the format required by the competition.

## General Guidelines

- Ensure that all modules and functions are well-documented.
- Follow consistent naming conventions and code style.
- Use type annotations for function signatures to improve code readability and maintainability.
