Predict Customer Churn
- Project Predict Customer Churn of ML DevOps Engineer Nanodegree Udacity
Project Description
Identify credit card customers that are most likely to churn.
Project files structure:
- data: csv-data of customers
- images: plots of feature importances, roc curves, prediction results
- logs: logs of churn_script_logging_and_tests.py
- models: load and save trained models
Installation Dependencies
- scikit-learn
- numpy
- pandas
- matplotlib
- seaborn
Running Files
How do you run your files? What should happen when you run your files?
churn_library.py: all necessary functions to predict credit card customers that are most likely to churn
python churn_library.py run all functions to predict credit card customers depending of 'Churn' column
run to:
- import csv data
- perform eda and save images
- train and save models
- plot and save feature importances
churn_script_logging_and_tests.py: test all functions of churn_library.py and save logging
python churn_script_logging_and_tests.py test all functions of churn_library and write logging file ./logs/churn_library.log attention overrides ./models