ucaiado / mlops-predict-churn-clean-code

Build a customer churn prediction model using clean code principles

Predict Customer Churn with Clean Code

This project is part of the ML DevOps Engineer Nanodegree program from Udacity. I developed a model to identify credit card customers that are most likely to churn, following coding (PEP8) and engineering best practices for implementing software (modular, documented, and tested) and the package also have the flexibility of being run interactively or from the command-line interface (CLI).

Install

To set up your environment to run the code in this repository, start by installing Docker in your machine. Then, start Docker Desktop and run.

$ make docker-build

Run

In a terminal or command window, navigate to the top-level project directory mlops-predict-churn-clean-code/ (that contains this README) and run the following command.

$ make tests

It will generate EDA plots in the images/eda/ folder, store some results also as images in the images/results/ directory, save the models trained in models/, and write the test logs in logs/churn_library.log file.

License

The contents of this repository are covered under the MIT License.