- Course: ML DevOps Engineer Nanodegree Udacity
- Python version: 3.11.5
The objective of this project is to implement best coding practices. For that, I should prove my skills in testing, logging, and best coding practices in the implementation of a pre-developed machine learning model. I was supposed to:
- Refactor the model code and create a Python file.
- Create a test and log file
- Give a good project description so anyone can run the code by reading this README.
Data
Churn database from Kaggle
Images
- EDA: data explore outputs
- Results: models outputs
Logs
Text file with Logging and Tests report
Models Logistic Regression and Random Forrest models (.plk files)
Guide
Notebook with project general instructions
churn_notebook.ipynb
The Python Notebook contains the code that needs refactoring
churn_library.py
Library of functions to find customers likely to churn.
churn_script_logging_and_tests.py
Contain unit tests for the churn_library.py functions. It also logs any errors and INFO messages.
How do you run your files? What should happen when you run your files?
To run the complete script with logging and tests, paste on the terminal:
$ ipython churn_script_logging_and_tests_solution.py
Or, if you want to run the library:
$ ipython churn_library.py