Predict Customer Churn

Project Predict Customer Churn in ML DevOps Engineer Nanodegree program by Udacity.

Project Description

Customer attrition, also known as customer churn, is the loss of clients or customers. Businesses measure and track churn as a percentage of lost customers compared to total number of customers over a given time period. Identifying and handling the customers about to churn can improve overall outcomes of the business.

This project seeks to answer the customer churn that is happening in the banking industry using clean code principles

The project is divided into the following sections:

Import Data
Exploratory data analysis and visualizations
Feature engineering to transform data
Building Models and evaulating performance

Project Components

There are two components in this project:

1. Churn Library

File churn_library.py:

Loads the bank_data dataset
Performs EDA and Visualizations
Feature Engineering
Model and Evaluation

2. Churn Script Logging and Testing

File churn_script_logging_and_testing.py:

Tests churn library
Logs activity

Running

Dependencies

List of libraries used for this project:

autopep8==1.5.7
joblib==0.11
matplotlib==2.1.0
numpy==1.12.1
pandas==0.23.3
pylint==2.9.6
scikit-learn==0.22
seaborn==0.8.1

Use the package manager pip to install the dependencies from the requirements.txt

pip install -r requirements.txt

Modeling

Run the following command to execute the main script

python churn_library.py

script execution generates

EDA plots are available in directory ./images/eda/
Model metrics are available in directory ./images/results/
Model pickle files are available in directory ./models/
Log files are available in directory ./logs/results.log

Testing and Logging

Run the following command to run the tests script

python churn_script_logging_and_tests.py

script execution generates

Log file ./logs/results.log

License

Distributed under the MIT License. See LICENSE for more information.