ML Project Structure

project_name/
├── README.md
├── requirements.txt
├── setup.py
├── .env.example
├── .gitignore
├── config/
│   ├── __init__.py
│   ├── config.yaml
│   ├── database.yaml
│   └── model_params.yaml
├── data/
│   ├── raw/                    # Original, immutable data
│   ├── interim/                # Intermediate processed data
│   ├── processed/              # Final, model-ready datasets
│   └── external/               # External reference data
├── notebooks/
│   ├── 01_data_exploration.ipynb
│   ├── 02_data_preprocessing.ipynb
│   ├── 03_feature_engineering.ipynb
│   ├── 04_model_development.ipynb
│   └── 05_model_evaluation.ipynb
├── src/
│   ├── __init__.py
│   ├── etl/
│   │   ├── __init__.py
│   │   ├── extract.py          # Data extraction logic
│   │   ├── transform.py        # Data transformation logic
│   │   └── load.py             # Data loading logic
│   ├── preprocessing/
│   │   ├── __init__.py
│   │   ├── cleaners.py         # Data cleaning functions
│   │   ├── encoders.py         # Categorical encoding
│   │   ├── scalers.py          # Feature scaling
│   │   └── feature_engineering.py
│   ├── models/
│   │   ├── __init__.py
│   │   ├── base_model.py       # Base model class
│   │   ├── regression_model.py # Regression model implementation
│   │   ├── classification_model.py
│   │   ├── train.py            # Training pipeline
│   │   └── predict.py          # Prediction pipeline
│   └── utils/
│       ├── __init__.py
│       ├── database.py         # Database connection utilities
│       ├── logging.py          # Logging configuration
│       ├── validation.py       # Data validation functions
│       └── helpers.py          # General utility functions
├── scripts/
│   ├── run_etl.py              # ETL pipeline runner
│   ├── train_model.py          # Model training script
│   ├── batch_predict.py        # Batch prediction script
│   └── data_quality_check.py   # Data quality validation
├── tests/
│   ├── __init__.py
│   ├── test_etl/
│   ├── test_preprocessing/
│   ├── test_models/
│   └── test_utils/
├── models/
│   ├── trained/                # Saved trained models
│   ├── experiments/            # Experimental model versions
│   └── baselines/              # Baseline model results
├── reports/
│   ├── figures/                # Generated plots and charts
│   ├── data_profiling/         # Data quality reports
│   └── model_performance/      # Model evaluation reports
├── logs/
│   ├── etl/                    # ETL process logs
│   ├── training/               # Model training logs
│   └── predictions/            # Prediction logs
└── docs/
    ├── data_dictionary.md
    ├── model_documentation.md
    ├── etl_documentation.md
    └── deployment_guide.md

Key Design Principles

Data Flow Organization

• Raw data stays immutable in data/raw/
• Interim data for intermediate processing steps
• Processed data for final model-ready datasets
• Clear separation between exploration (notebooks) and production code (src/)

Modular Architecture

• ETL module handles all data pipeline operations
• Preprocessing module contains reusable data cleaning and transformation functions
• Models module implements training and prediction logic
• Utils module provides common utilities across the project

Development Workflow

• Notebooks for experimentation and analysis
• Scripts for running production pipelines
• Tests to ensure code reliability
• Config files for environment-specific settings

Best Practices Included

• Version control friendly structure
• Separate environments for different stages
• Comprehensive logging and monitoring
• Documentation and model tracking
• Scalable and maintainable codebase

Usage Examples

# Run ETL pipeline
python scripts/run_etl.py --config config/config.yaml

# Train regression model
python scripts/train_model.py --data data/processed/train.csv

# Run batch predictions
python scripts/batch_predict.py --model models/trained/regression_v1.pkl

.gitignore content:

# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg

# Virtual environments
venv/
env/
ENV/
.venv/

# Environment variables
.env
.env.local
.env.*.local

# IDE files
.vscode/
.idea/
*.swp
*.swo
*~

# Jupyter Notebook
.ipynb_checkpoints

# Data files (exclude large datasets)
data/raw/*
data/interim/*
data/processed/*
# Keep directory structure
!data/raw/.gitkeep
!data/interim/.gitkeep
!data/processed/.gitkeep

# Model files (exclude large trained models)
models/trained/*
models/experiments/*
!models/trained/.gitkeep
!models/experiments/.gitkeep

# Logs
logs/
*.log

# OS generated files
.DS_Store
.DS_Store?
._*
.Spotlight-V100
.Trashes
ehthumbs.db
Thumbs.db

# Temporary files
*.tmp
*.temp