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In this project, you will implement your learnings to identify credit card customers that are most likely to churn. The completed project will include a Python package for a machine learning project that follows coding (PEP8) and engineering best practices for implementing software (modular, documented, and tested).

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Predict Customer Churn

  • Project Predict Customer Churn of ML DevOps Engineer Nanodegree Udacity

Project Description

In this project, you will implement your learnings to identify credit card customers that are most likely to churn. The completed project will include a Python package for a machine learning project that follows coding (PEP8) and engineering best practices for implementing software (modular, documented, and tested). The package will also have the flexibility of being run interactively or from the command-line interface (CLI).

This project will give you practice using your skills for testing, logging, and best coding practices from this lesson. It will also introduce you to a problem data scientists across companies face all the time. How do we identify (and later intervene with) customers who are likely to churn?

DataFrame

kaggle

Files and data description

Overview of the files and data present in the root directory

file-overview

Running Files

We will use git and conda:

  1. Clone the repository

  2. Create a conda environment

  3. Activate the conda environment

Install the linter and auto-formatter


~/project$ pip install pylint
~/project$ pip install autopep8

Run python files


~/project$ python churn_library.py
~/project$ python python_script_logging_and_tests.py

check the pylint score using the below


~/project$ pylint churn_library.py --disable=invalid-name,too-many-arguments

~/project$ pylint churn_script_logging_and_tests.py --disable=invalid-name,too-many-arguments

To assist with meeting pep 8 guidelines, use autopep8 via the command line commands below


~/project$ autopep8 --in-place --aggressive --aggressive churn_script_logging_and_tests.py

~/project$ autopep8 --in-place --aggressive --aggressive churn_library.py

References

Kaggle

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In this project, you will implement your learnings to identify credit card customers that are most likely to churn. The completed project will include a Python package for a machine learning project that follows coding (PEP8) and engineering best practices for implementing software (modular, documented, and tested).

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