Predict Customer Churn

• Project Predict Customer Churn of ML DevOps Engineer Nanodegree Udacity

Project Description

In this project, we created a model that can identify credit card customers that are most likely to churn. But the main objective to implement best coding practices.The completed project include a Python package for a machine learning project that follows coding (PEP8) and engineering best practices for implementing software (modular, documented, and tested).

Files and data description

The structure of this project directory tree is displayed as follows:

.
├── churn_library.py
├── churn_notebook.ipynb
├── Guide.ipynb
├── churn_script_logging_and_tests.py
├── 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_importances.png
│       ├── logistic_results.png
│       ├── rf_results.png
│       └── roc_curve_result.png
├── logs
│   └── churn_library.log
├── models
│   ├── logistic_model.pkl
│   └── rfc_model.pkl
├── README.md
├── requirements_py3.6.txt
└── requirements_py3.8.txt

• Folders

  • data: Folder that contains the data in csv format
  • images: Main folder
  • eda: Folder that is used to store the results of visualizations of numerical results
  • results: Folder that is used to store the training evaluation results
  • logs: Folder that stores the logs for the test results on the churn_library.py file
  • models: Folder is used to store model objects

• Files

  • churn_library.py: The churn_library.py is a library of functions to find customers who are likely to churn.
  • churn_notebook.ipynb: the churn_notebook.ipynb file containing the solution to identify credit card customers that are most likely to churn, but without implementing the engineering and software best practices.
  • Guide.ipynb: The Guide.ipynb is the starting point for the project
  • churn_script_logging_and_tests.py: The churn_script_logging_and_tests.py contain unit tests for the churn_library.py functions

Running Files

Clone the project
git clone https://github.com/PhilippeMitch/Predict-Customer-Churn.git
This command will load the project into your personel computer

Install the required libraries
pip install -r requirements_py3.8.txt
With this command you will install all the required library.

Run the workflow
python churn_library.py

Testing and Logging
python churn_script_logging_and_tests.py
This command will test each of the functions and provide any errors to a file stored in the /logs folder.