In Python machine learning, how do I set up the project structure?

When setting up a machine learning project in Python, it's crucial to follow a structured approach to ensure clarity and maintainability. Here's a suggested project structure:

    ├── data/                  # Directory for storing data files
    │   ├── raw/              # Original, immutable data dump
    │   └── processed/        # Cleaned and processed data
    ├── notebooks/            # Jupyter notebooks for experimentation
    ├── src/                  # Source code files
    │   ├── __init__.py       # Makes src a Python package
    │   ├── data/             # Scripts for data loading and preprocessing
    │   ├── features/         # Scripts for feature engineering
    │   ├── models/           # Scripts for model training and evaluation
    │   └── visualization/     # Scripts for visualizations
    ├── requirements.txt      # List of project dependencies
    ├── setup.py              # Setup file for package distribution
    ├── README.md             # Project overview and instructions
    └── .gitignore            # Git ignore file