• Project Predict Customer Churn of ML DevOps Engineer Nanodegree Udacity To produce modular, documented, and tested tested code for Customer Churn Prediction

To predict customer churn. The data can be found on Kaggle

The repo contains the following files:

• data
  • bank_data.csv
• images
  • eda
    • churn_distribution.png
    • customer_age_distribution.png
    • heatmap.png
    • marital_status_distribution.png
    • total_transaction_distribution.png
  • results
    • feature_importance.png
    • logistics_results.png
    • rf_results.png
    • roc_curve_result.png
• logs
  • churn_library.log
• models
  • logistic_model.pkl
  • rfc_model.pkl
• churn_library.py
• churn_notebook.ipynb
• churn_script_logging_and_tests.py

The churn_notebook.ipynb contains the base code and churn_library.py the modular code for production. The unit tests are in churn_script_logging_and_tests.py.

Pickled models are stored in the models folder.

The logs are contained in logs/churn_library.log and graphs created during the EDA or depicting results are saved in images

The file can be run using: ipython churn_library.py

With pytest, unit tests and logs for churn_library.py can be obtained by: pytest churn_script_logging_and_tests.py

Pylint test scores can be obtained by running: pylint churn_library.py pylint churn_script_logging_and_tests.py

scikit-learn==0.22 shap==0.39.0 pylint==2.9.6 autopep8==1.5.7 seaborn==0.8.1 numpy==1.12.1 matplotlib==2.1.0 pandas==0.23.3