• Project Predict Customer Churn of ML DevOps Engineer Nanodegree Udacity

This project aims to demonstrates the workflow of coding best practices for a standard data science task. This includes best practices on; general workflow, testing, logging, and PEP8 standards.

Running the below will install all dependencies necessary to run both python scripts.

pip install -r requirements.txt

Running the below will test each function from churn_library.py. During this process it will write log results in the ./logs directory.

ipython churn_script_logging_and_tests.py

Running the below will step through each function of the data science process. This includes; exploratory data analysis (EDA), feature transformations, model training and saving results.

ipython churn_library.py

This will generate:

• EDA plots in the ./images/eda/ directory
• training results in the ./images/results/ directory
• models that are saved in the ./models/ directory
• feature importances results in the ./images/results/ directory