The project Predict Customer Churn is delivered to demonstrate clean code principles as part of ML DevOps Engineer Nanodegree Udacity. The main scope of the project is to implement a machine learning algorithm to identify credit card customers that are likley to churn.

The main objective of the implementation is to demonstrate clean code principles with focus on testing, logging and coding best practices. The project structure is based on the following approach,

• Load the CSV data and perform Exploratory data analysis
• Prepare the classification label based on the given input features
• perform feature engineering to prepare the data for model training
• Train classification model based on Random forest and logistic regression and save the best model
• Identify the key input features affecting the classification

Overview of the files and data present in the root directory.

Setup the environment and and install dependencies based on the provided requirments_py3.6.txt

• create a virtual environment using the following command

python -m venv <env-name>

• Activate the virtual environment

source <env-name>/bin/activate

• Install the required dependencies

pip3 install -r requirments_py3.6.txt

• To run the churn library

python churn_library.py

• To test the churn library

pytest

• additionally to meet pep8 standard

autopep8 --in-place --aggressive --aggressive test_churn_script_logging_and_tests.py
autopep8 --in-place --aggressive --aggressive churn_library.py

• additionally to perform code analysis

pylint churn_library.py
pylint test_churn_script_logging_and_tests.py