Introduction to Cookiecutter for Data Science Projects

project-name/
├── data/
│   ├── raw/                <- Original data
│   ├── processed/          <- Finalized datasets
│   └── external/           <- Data from third parties
├── notebooks/              <- Jupyter notebooks
├── src/                    <- Source code for project logic
├── reports/
│   └── figures/            <- Generated visualizations
├── README.md               <- Project overview
├── .gitignore              <- Git configuration
└── requirements.txt        <- Python dependencies

pipx install cookiecutter-data-science

pip install cookiecutter-data-science

# Create the directory if it doesn't exist
mkdir AllProjects

# Navigate into the directory
cd AllProjects

ccds

project_name [Default Project]: Customer Churn Analysis
module_name [customer_churn_analysis]:
author_name [Your Name]: Alex Johnson
author_email [your.email@example.com]: alex.johnson@example.com
description [A short description of the project.]: Analyzing factors contributing to customer churn.
open_source_license [MIT]:

ls

cd customer-churn-analysis

customer-churn-analysis/
├── LICENSE
├── Makefile
├── README.md
├── data
│   ├── external
│   ├── interim
│   ├── processed
│   └── raw
├── docs
│   └── mkdocs.yml
├── models
├── notebooks
├── pyproject.toml
├── references
├── reports
│   └── figures
├── requirements.txt
├── setup.cfg
└── customer_churn_analysis
    ├── __init__.py
    ├── config.py
    ├── dataset.py
    ├── features.py
    ├── modeling
    │   ├── __init__.py
    │   ├── predict.py
    │   └── train.py
    └── plots.py

# Customer Churn Analysis

Analyzing factors contributing to customer churn.

## Project Overview

This project aims to identify patterns and factors that lead to customer churn using historical data.

## Installation

To install the required packages:

```bash
pip install -r requirements.txt

##### **Makefile**

Un `Makefile` contient des commandes qui automatisent des tâches courantes. Par exemple :

```makefile
install:
	pip install -r requirements.txt

data:
	python customer_churn_analysis/dataset.py

train:
	python customer_churn_analysis/modeling/train.py

[tool.black]
line-length = 88
target-version = ['py38']

[tool.isort]
profile = "black"

DATA_PATH = 'data'
RAW_DATA_PATH = f'{DATA_PATH}/raw'
PROCESSED_DATA_PATH = f'{DATA_PATH}/processed'

import pandas as pd
from config import RAW_DATA_PATH, PROCESSED_DATA_PATH

def load_raw_data():
    # Implement code to load raw data
    pass

def preprocess_data():
    # Implement preprocessing steps
    pass

if __name__ == '__main__':
    load_raw_data()
    preprocess_data()

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import joblib
from customer_churn_analysis.features import prepare_features
from config import MODELS_PATH

def train_model():
    X, y = prepare_features()
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
    model = RandomForestClassifier()
    model.fit(X_train, y_train)
    joblib.dump(model, f'{MODELS_PATH}/random_forest.pkl')

if __name__ == '__main__':
    train_model()

your-template-name/
├── cookiecutter.json
├── {{cookiecutter.variable_name}}/
│   ├── file1.txt
│   └── subfolder/
│       └── file2.txt
└── other_files_or_folders/

{
    "project_name": "my_project",
    "author_name": "Your Name",
    "email": "your.email@example.com",
    "description": "A short description of the project."
}

# Navigate to your templates directory
cd path/to/cookiecutter-templates/

# Create a new folder for your template
mkdir my-cookiecutter-template
cd my-cookiecutter-template

mkdir "{{cookiecutter.project_name}}"

my-cookiecutter-template/
└── {{cookiecutter.project_name}}/

# Inside my-cookiecutter-template/
touch cookiecutter.json

{
    "project_name": "my_data_science_project",
    "author_name": "Your Name",
    "email": "your.email@example.com",
    "description": "A short description of the project."
}

my-cookiecutter-template/
├── cookiecutter.json
└── {{cookiecutter.project_name}}/

# Inside my-cookiecutter-template/{{cookiecutter.project_name}}/
touch README.md

# {{cookiecutter.project_name}}

{{cookiecutter.description}}

## Project Structure

- **data/**: Data used in the project.
- **notebooks/**: Jupyter notebooks.
- **src/**: Source code files.

## Author

**{{cookiecutter.author_name}}**  
Email: {{cookiecutter.email}}

# Inside my-cookiecutter-template/{{cookiecutter.project_name}}/
mkdir data notebooks src

# Inside data directory
mkdir data/raw data/processed

my-cookiecutter-template/
├── cookiecutter.json
└── {{cookiecutter.project_name}}/
    ├── README.md
    ├── data/
    │   ├── raw/
    │   └── processed/
    ├── notebooks/
    └── src/

# Inside {{cookiecutter.project_name}}/
touch requirements.txt

# requirements.txt

pandas
numpy
matplotlib

# Inside src/
touch main.py

# main.py

"""
Project: {{cookiecutter.project_name}}
Author: {{cookiecutter.author_name}}
Description: {{cookiecutter.description}}
"""

def main():
    print("Hello, world!")

if __name__ == "__main__":
    main()

cookiecutter path/to/my-cookiecutter-template

project_name [my_data_science_project]:
author_name [Your Name]:
email [your.email@example.com]:
description [A short description of the project.]:

project_name [my_data_science_project]: heart-disease-analysis
author_name [Your Name]: Alice Smith
email [your.email@example.com]: alice.smith@example.com
description [A short description of the project.]: Analysis of heart disease dataset.

heart-disease-analysis/
├── README.md
├── data/
│   ├── raw/
│   └── processed/
├── notebooks/
├── requirements.txt
└── src/
    └── main.py

# heart-disease-analysis

Analysis of heart disease dataset.

## Project Structure

- **data/**: Data used in the project.
- **notebooks/**: Jupyter notebooks.
- **src/**: Source code files.

## Author

**Alice Smith**  
Email: alice.smith@example.com

"""
Project: heart-disease-analysis
Author: Alice Smith
Description: Analysis of heart disease dataset.
"""

def main():
    print("Hello, world!")

if __name__ == "__main__":
    main()

$ cookiecutter path/to/data_science_template
project_name [My Project]: Sales Forecast
author_name [Your Name]: Bob Johnson
author_email [your.email@example.com]: bob.johnson@example.com
team:
1 - Data Team
2 - AI Team
3 - Research Team
Choose from 1, 2, 3 [1]: 2

# Sales Forecast

**Author**: Bob Johnson  
**Email**: <bob.johnson@example.com>

## Team Members

- AI Team Member A
- AI Team Member B

## Overview

This project is about...

{
  "project_name": "my_data_science_project",
  "author_name": "Your Name",
  "description": "A short description of the project"
}

# {{cookiecutter.project_name}}

**Author**: {{cookiecutter.author_name}}

## Description

{{cookiecutter.description}}

## Project Structure

pandas
numpy
matplotlib
scikit-learn

project_name [my_data_science_project]:
author_name [Your Name]:
description [A short description of the project]:

customer_segmentation/
├── data/
│   ├── raw/
│   └── processed/
├── notebooks/
├── src/
├── README.md
└── requirements.txt

project-name/
├── data/
│   ├── raw/                <- Original data
│   ├── processed/          <- Finalized datasets
│   └── external/           <- Data from third parties
├── notebooks/              <- Jupyter notebooks
├── src/                    <- Source code for project logic
├── reports/
│   └── figures/            <- Generated visualizations
├── README.md               <- Project overview
├── .gitignore              <- Git configuration
└── requirements.txt        <- Python dependencies

pipx install cookiecutter-data-science

pip install cookiecutter-data-science

# Create the directory if it doesn't exist
mkdir AllProjects

# Navigate into the directory
cd AllProjects

ccds

project_name [Default Project]: Customer Churn Analysis
module_name [customer_churn_analysis]:
author_name [Your Name]: Alex Johnson
author_email [your.email@example.com]: alex.johnson@example.com
description [A short description of the project.]: Analyzing factors contributing to customer churn.
open_source_license [MIT]:

ls

cd customer-churn-analysis

customer-churn-analysis/
├── LICENSE
├── Makefile
├── README.md
├── data
│   ├── external
│   ├── interim
│   ├── processed
│   └── raw
├── docs
│   └── mkdocs.yml
├── models
├── notebooks
├── pyproject.toml
├── references
├── reports
│   └── figures
├── requirements.txt
├── setup.cfg
└── customer_churn_analysis
    ├── __init__.py
    ├── config.py
    ├── dataset.py
    ├── features.py
    ├── modeling
    │   ├── __init__.py
    │   ├── predict.py
    │   └── train.py
    └── plots.py

# Customer Churn Analysis

Analyzing factors contributing to customer churn.

## Project Overview

This project aims to identify patterns and factors that lead to customer churn using historical data.

## Installation

To install the required packages:

```bash
pip install -r requirements.txt

##### **Makefile**

A `Makefile` contains commands that automate common tasks. For example:

```makefile
install:
	pip install -r requirements.txt

data:
	python customer_churn_analysis/dataset.py

train:
	python customer_churn_analysis/modeling/train.py

[tool.black]
line-length = 88
target-version = ['py38']

[tool.isort]
profile = "black"

DATA_PATH = 'data'
RAW_DATA_PATH = f'{DATA_PATH}/raw'
PROCESSED_DATA_PATH = f'{DATA_PATH}/processed'

import pandas as pd
from config import RAW_DATA_PATH, PROCESSED_DATA_PATH

def load_raw_data():
    # Implement code to load raw data
    pass

def preprocess_data():
    # Implement preprocessing steps
    pass

if __name__ == '__main__':
    load_raw_data()
    preprocess_data()

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import joblib
from customer_churn_analysis.features import prepare_features
from config import MODELS_PATH

def train_model():
    X, y = prepare_features()
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
    model = RandomForestClassifier()
    model.fit(X_train, y_train)
    joblib.dump(model, f'{MODELS_PATH}/random_forest.pkl')

if __name__ == '__main__':
    train_model()

your-template-name/
├── cookiecutter.json
├── {{cookiecutter.variable_name}}/
│   ├── file1.txt
│   └── subfolder/
│       └── file2.txt
└── other_files_or_folders/

{
    "project_name": "my_project",
    "author_name": "Your Name",
    "email": "your.email@example.com",
    "description": "A short description of the project."
}

# Navigate to your templates directory
cd path/to/cookiecutter-templates/

# Create a new folder for your template
mkdir my-cookiecutter-template
cd my-cookiecutter-template

mkdir "{{cookiecutter.project_name}}"

my-cookiecutter-template/
└── {{cookiecutter.project_name}}/

# Inside my-cookiecutter-template/
touch cookiecutter.json

{
    "project_name": "my_data_science_project",
    "author_name": "Your Name",
    "email": "your.email@example.com",
    "description": "A short description of the project."
}

my-cookiecutter-template/
├── cookiecutter.json
└── {{cookiecutter.project_name}}/

# Inside my-cookiecutter-template/{{cookiecutter.project_name}}/
touch README.md

# {{cookiecutter.project_name}}

{{cookiecutter.description}}

## Project Structure

- **data/**: Data used in the project.
- **notebooks/**: Jupyter notebooks.
- **src/**: Source code files.

## Author

**{{cookiecutter.author_name}}**  
Email: {{cookiecutter.email}}

# Inside my-cookiecutter-template/{{cookiecutter.project_name}}/
mkdir data notebooks src

# Inside data directory
mkdir data/raw data/processed

my-cookiecutter-template/
├── cookiecutter.json
└── {{cookiecutter.project_name}}/
    ├── README.md
    ├── data/
    │   ├── raw/
    │   └── processed/
    ├── notebooks/
    └── src/

# Inside {{cookiecutter.project_name}}/
touch requirements.txt

# requirements.txt

pandas
numpy
matplotlib

# Inside src/
touch main.py

# main.py

"""
Project: {{cookiecutter.project_name}}
Author: {{cookiecutter.author_name}}
Description: {{cookiecutter.description}}
"""

def main():
    print("Hello, world!")

if __name__ == "__main__":
    main()

cookiecutter path/to/my-cookiecutter-template

project_name [my_data_science_project]:
author_name [Your Name]:
email [your.email@example.com]:
description [A short description of the project.]:

project_name [my_data_science_project]: heart-disease-analysis
author_name [Your Name]: Alice Smith
email [your.email@example.com]: alice.smith@example.com
description [A short description of the project.]: Analysis of heart disease dataset.

heart-disease-analysis/
├── README.md
├── data/
│   ├── raw/
│   └── processed/
├── notebooks/
├── requirements.txt
└── src/
    └── main.py

# heart-disease-analysis

Analysis of heart disease dataset.

## Project Structure

- **data/**: Data used in the project.
- **notebooks/**: Jupyter notebooks.
- **src/**: Source code files.

## Author

**Alice Smith**  
Email: alice.smith@example.com

"""
Project: heart-disease-analysis
Author: Alice Smith
Description: Analysis of heart disease dataset.
"""

def main():
    print("Hello, world!")

if __name__ == "__main__":
    main()

$ cookiecutter path/to/data_science_template
project_name [My Project]: Sales Forecast
author_name [Your Name]: Bob Johnson
author_email [your.email@example.com]: bob.johnson@example.com
team:
1 - Data Team
2 - AI Team
3 - Research Team
Choose from 1, 2, 3 [1]: 2

# Sales Forecast

**Author**: Bob Johnson  
**Email**: <bob.johnson@example.com>

## Team Members

- AI Team Member A
- AI Team Member B

## Overview

This project is about...

{
  "project_name": "my_data_science_project",
  "author_name": "Your Name",
  "description": "A short description of the project"
}

# {{cookiecutter.project_name}}

**Author**: {{cookiecutter.author_name}}

## Description

{{cookiecutter.description}}

## Project Structure

pandas
numpy
matplotlib
scikit-learn

project_name [my_data_science_project]:
author_name [Your Name]:
description [A short description of the project]:

customer_segmentation/
├── data/
│   ├── raw/
│   └── processed/
├── notebooks/
├── src/
├── README.md
└── requirements.txt