Data Science Best Practices - Sample

A comprehensive, production-ready data science project template demonstrating industry best practices, reusable workflows, and the Cookiecutter Data Science structure.

🎯 Overview

This repository provides executable Jupyter notebooks and reusable code modules for building scalable data science projects. It follows the Cookiecutter Data Science framework and demonstrates techniques that drive 80% of model performance through proper feature engineering, documentation, and project organization.

Key Features

• ✅ Complete ML Pipeline: From data exploration to model deployment
• ✅ Executable Notebooks: Ready-to-run Jupyter notebooks with sample data
• ✅ Best Practices: Industry-standard project structure and workflows
• ✅ Business Impact Focus: ROI analysis and business metric translation
• ✅ Comprehensive Documentation: Model cards, API docs, and guides

📊 Project Structure

data-science-examples/
├── data/
│   ├── raw/              # Original, immutable data
│   ├── interim/          # Intermediate transformations
│   ├── processed/        # Final datasets for modeling
│   └── external/         # Third-party sources
├── notebooks/            # Jupyter notebooks for exploration
│   ├── 01-data-exploration-eda.ipynb
│   ├── 02-feature-engineering.ipynb
│   └── 03-model-training-evaluation.ipynb
├── src/                  # Source code for production
│   ├── data/             # Data loading and processing
│   ├── features/         # Feature engineering
│   ├── models/           # Training and prediction
│   └── visualization/    # Plotting and reporting
├── models/               # Trained and serialized models
├── reports/              # Generated analysis and figures
├── docs/                 # Project documentation
│   ├── project_documentation_template.md
│   └── model_card_template.md
└── tests/                # Unit and integration tests

🚀 Quick Start

Prerequisites

• Python 3.11+
• pip or conda package manager

Installation

# Clone the repository
git clone https://github.com/yourusername/data-science-examples.git
cd data-science-examples

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Launch Jupyter
jupyter notebook

Running the Notebooks

The notebooks are designed to run sequentially:

1. 01-data-exploration-eda.ipynb: Load data, perform EDA, identify patterns
2. 02-feature-engineering.ipynb: Create interaction, temporal, and domain-specific features
3. 03-model-training-evaluation.ipynb: Train models, evaluate performance, calculate business impact

Each notebook generates sample data if source files don't exist, so you can run them immediately without external datasets.

📓 Notebook Descriptions

1. Data Exploration and EDA

Purpose: Understand data quality, distributions, and relationships

Key Sections:

• Data loading and quality assessment
• Descriptive statistics and distributions
• Target variable analysis (churn rate: ~27%)
• Correlation analysis
• Feature relationships with target
• Categorical feature analysis
• Key insights summary

Outputs:

• Explored dataset saved to data/interim/
• Visualization plots for distributions and relationships

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2. Feature Engineering

Purpose: Create features that drive 80% of model performance

Key Sections:

• Interaction Features: Tenure-based ratios, spending patterns, customer value segments
• Temporal Features: Customer lifecycle stages, tenure groups, age groups
• Domain-Specific Features: Contract commitment scores, payment reliability, service adoption
• Encoding: One-hot encoding for categorical variables
• Scaling: StandardScaler for numerical features
• Feature Selection: ANOVA F-test and Mutual Information

Outputs:

• Full feature set: data/processed/customer_data_features.csv
• Selected features: data/processed/customer_data_selected_features.csv

Impact: Expected 20-50% accuracy improvement over baseline

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3. Model Training and Evaluation

Purpose: Train, evaluate, and optimize machine learning models

Key Sections:

• Train-test split with stratification
• Class imbalance handling (upsampling)
• Multi-model comparison (Logistic Regression, Random Forest, Gradient Boosting)
• Comprehensive evaluation metrics (ROC AUC, F1, Precision, Recall)
• Confusion matrix analysis
• ROC and Precision-Recall curves
• Feature importance analysis
• Business Impact Analysis: ROI calculation, revenue saved, campaign costs
• Hyperparameter tuning with GridSearchCV
• Model serialization for deployment

Outputs:

• Trained model: models/random_forest_model.pkl (or best model)
• Feature names: models/feature_names.txt
• Performance metrics and business impact summary

Business Metrics:

• Net Benefit calculation
• ROI percentage
• Revenue saved from retention
• Campaign cost analysis

🔧 Source Code Modules

Data Processing (src/data/)

from src.data.make_dataset import load_raw_data, clean_data, save_processed_data

• make_dataset.py: Load, clean, and save datasets
• Implements data immutability principle (raw data never modified)

Feature Engineering (src/features/)

from src.features.build_features import create_interaction_features, create_temporal_features

• build_features.py: Reusable feature engineering functions
• Modular design for easy integration into pipelines

Model Training (src/models/)

from src.models.train_model import train_model, evaluate_model, save_model

• train_model.py: Model training, evaluation, and persistence
• Supports multiple algorithms and hyperparameter tuning

📈 Business Impact

This project demonstrates how to translate technical metrics into business value:

Metric	Technical	Business Translation
Accuracy	90%	Meaningless without context
True Positives	54 customers	$54,000 revenue saved
False Positives	20 customers	$1,000 campaign cost
ROI	N/A	5,300% return on investment

Example Business Impact (from notebook 03)

• Customers Correctly Identified: 54 churning customers
• Revenue Saved: $54,000 (54 × $1,000 LTV)
• Campaign Cost: $3,700 (74 campaigns × $50)
• Net Benefit: $50,300
• ROI: 1,360%

📚 Documentation

Project Documentation

See docs/project_documentation_template.md for comprehensive project documentation including:

• Project overview and objectives
• Data sources and descriptions
• Methodology and approach
• Model architecture and hyperparameters
• Evaluation metrics and results
• Deployment instructions
• Maintenance and monitoring

Model Card

See docs/model_card_template.md for model-specific documentation including:

• Model details and intended use
• Training data and evaluation data
• Performance metrics across subgroups
• Ethical considerations and limitations
• Bias and fairness assessments

🧪 Testing

# Run all tests
pytest tests/

# Run with coverage
pytest --cov=src tests/

# Run specific test file
pytest tests/test_features.py

🔄 Reproducibility

This project ensures reproducibility through:

1. Data Immutability: Raw data never modified in place
2. Version Control: Git for code, DVC for data (recommended)
3. Random Seeds: Fixed seeds (42) for consistent results
4. Environment Management: requirements.txt with pinned versions
5. Documentation: Comprehensive docs and inline comments

📊 Key Insights

Feature Engineering Impact

• 80% of model performance comes from feature engineering
• 20-50% accuracy gains from engineered features
• 2-5% gains from algorithm optimization alone

Project Organization Benefits

• 70% reduction in onboarding time with standardized structure
• 40% faster project completion with frameworks
• 3× higher deployment success rates

Failure Prevention

• 87% of data science projects fail without proper structure
• 30% failure rate with best practices implemented
• 60% of time wasted searching for data without organization

🚀 Deployment

Model Serving

import joblib
import pandas as pd

# Load model
model = joblib.load('models/random_forest_model.pkl')

# Load feature names
with open('models/feature_names.txt', 'r') as f:
    feature_names = f.read().splitlines()

# Make predictions
def predict_churn(customer_data):
    """Predict churn probability for a customer"""
    X = pd.DataFrame([customer_data], columns=feature_names)
    churn_probability = model.predict_proba(X)[0][1]
    return churn_probability

API Integration

For production deployment, consider:

• Flask/FastAPI: REST API for model serving
• Docker: Containerization for consistent environments
• Monitoring: Track prediction accuracy, data drift, feature distributions
• Retraining: Scheduled retraining pipeline (quarterly or on performance degradation)

🤝 Contributing

Contributions are welcome! Please follow these guidelines:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Cookiecutter Data Science: Project structure template
• Scikit-learn: Machine learning library
• Pandas & NumPy: Data manipulation
• Matplotlib & Seaborn: Visualization

📞 Contact

For questions or feedback:

• Open an issue in this repository
• Email: [email protected]
• LinkedIn: Samwel Munyingi

🔗 Resources

• Cookiecutter Data Science
• Data Science Best Practices
• Model Cards for Model Reporting
• ML Ops Best Practices

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