ML Project Starter

This is a simple, modular machine learning project template designed for training purposes. It implements best practices in Python and machine learning development.

Project Structure

MachineLearningWorkflow/
├── data/                   # Folder for datasets
├── notebooks/              # Jupyter notebooks for experimentation
├── src/                    # Main Python package
│   ├── __init__.py
│   ├── preprocess.py   # Data cleaning and transformation
│   ├── model.py        # Define train/test functions and model architectures
│   ├── evaluate.py     # Functions for accuracy, precision, recall, etc.
│   └── utils/              # Utility functions
│       ├── __init__.py
│       └── helpers.py
├── tests/                  # Unit tests
│   └── run_tests.py
├── README.md               # Project overview
└── requirements.txt        # Dependencies

Getting Started

Prerequisites

• Python 3.8 or higher
• Libraries: Install dependencies from requirements.txt using:

  pip install -r requirements.txt

How to Use

1. Clone the repository.
2. Place your dataset in the data/ folder.
3. Follow the Jupyter notebooks in the notebooks/ folder to understand the pipeline.
4. Modify the modules in the src/ folder to customize the pipeline.

Components

1. Data Preprocessing

Located in src/preprocess.py. This module includes:

• Functions for data cleaning, missing value handling, and feature scaling.
• Splitting datasets into training and testing sets.

2. Model Training

Located in src/model.py. This module includes:

• Definitions for different machine learning models.
• Training and testing pipelines.

3. Evaluation Metrics

Located in src/evaluate.py. This module includes:

• Functions for calculating performance metrics like accuracy, precision, recall, and F1-score.
• Visualization tools for confusion matrices and learning curves.

4. Utilities

Located in src/helpers.py. This module includes:

• Helper functions for logging, model saving/loading, and miscellaneous utilities.

Contributing

Trainees are encouraged to:

• Extend modules by adding new functionalities.
• Experiment with different datasets and models.
• Write unit tests for their additions in the tests/ folder.