setup_code.py

"""
Setup script for Customer Segmentation System.
This script creates the initial directory structure and files.
"""

import os
import sys


def create_project_structure():
    """
    Create the initial directory structure and files for the project.
    """
    # Create project directory structure
    directories = [
        "data/raw",
        "data/processed",
        "docs",
        "notebooks",
        "src",
        "models",
        "reports/figures",
    ]

    for directory in directories:
        os.makedirs(directory, exist_ok=True)
        print(f"Created directory: {directory}")

    # Create the directory structure for src
    src_directories = [
        "src/data",
        "src/features",
        "src/models",
        "src/visualization",
        "src/utils",
    ]

    for directory in src_directories:
        os.makedirs(directory, exist_ok=True)
        # Create __init__.py files
        with open(f"{directory}/__init__.py", "w") as f:
            f.write(f"# {directory} module\n")
        print(f"Created {directory}")

    # Create main package __init__.py
    with open("src/__init__.py", "w") as f:
        f.write("# Customer Segmentation package\n")

    # Create empty files
    files = ["requirements.txt", "README.md", ".gitignore", "run.py"]
    for file in files:
        with open(file, "w") as f:
            pass
        print(f"Created file: {file}")

    # Write requirements
    with open("requirements.txt", "w") as f:
        f.write(
            """
            numpy==1.24.3
            pandas==2.0.2
            matplotlib==3.7.1
            seaborn==0.12.2
            plotly==5.15.0
            scikit-learn==1.2.2
            streamlit==1.24.0
            jupyter==1.0.0
            """
        )

    # Create basic README
    with open("README.md", "w") as f:
        f.write(
            """# Customer Segmentation System

        A modular, extensible customer segmentation system that analyzes customer purchasing behavior and creates actionable customer segments. Built using Python, Streamlit, and scikit-learn.

        ## Features

        - **Data Loading & Preprocessing**: Clean and preprocess raw retail transaction data
        - **Feature Engineering**: Create meaningful customer-level features
        - **Customer Clustering**: Segment customers using KMeans clustering
        - **Segment Interpretation**: Automatically interpret clusters and create customer personas
        - **Dynamic Segmentation**: Update segments as new data becomes available
        - **Interactive Visualization**: Explore segments through an intuitive Streamlit dashboard
        - **Marketing Recommendations**: Get actionable marketing strategies for each segment

        ## Project Structure

        ```
        .
        ├── data/                 # Data directory
        │   ├── raw/              # Raw data files
        │   └── processed/        # Processed data files
        ├── models/               # Saved models
        ├── reports/              # Reports and visualizations
        │   └── figures/          # Generated figures
        ├── src/                  # Source code
        │   ├── data/             # Data loading and preprocessing
        │   ├── features/         # Feature engineering
        │   ├── models/           # Clustering models
        │   ├── visualization/    # Visualization and dashboard
        │   └── utils/            # Helper utilities
        ├── requirements.txt      # Project dependencies
        ├── README.md             # Project documentation
        └── run.py                # Script to run the application
        ```
        """
        )

    # Create a basic .gitignore
    with open(".gitignore", "w") as f:
        f.write(
            """# Python
        __pycache__/
        *.py[cod]
        *$py.class
        *.so
        .Python
        env/
        build/
        develop-eggs/
        dist/
        downloads/
        eggs/
        .eggs/
        lib/
        lib64/
        parts/
        sdist/
        var/
        *.egg-info/
        .installed.cfg
        *.egg

        # Jupyter Notebook
        .ipynb_checkpoints

        # Data files - uncomment if you want to ignore data files
        # data/raw/
        # data/processed/

        # Model files - uncomment if you want to ignore model files
        # models/

        # Virtual Environment
        venv/
        env/
        ENV/

        # IDE files
        .idea/
        .vscode/
        *.swp
        *.swo
        """
        )


if __name__ == "__main__":
    create_project_structure()