https://github.com/eljandoubi/predict-customer-churn-with-clean-code

Clean Code Principles
https://github.com/eljandoubi/predict-customer-churn-with-clean-code

autopep8 logging numpy pandas pylint pytest scikit-learn

Last synced: 6 days ago
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Clean Code Principles

• Host: GitHub
• URL: https://github.com/eljandoubi/predict-customer-churn-with-clean-code
• Owner: eljandoubi
• License: apache-2.0
• Created: 2023-08-30T15:20:28.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-08-31T14:57:41.000Z (over 1 year ago)
• Last Synced: 2024-11-25T01:09:47.456Z (2 months ago)
• Topics: autopep8, logging, numpy, pandas, pylint, pytest, scikit-learn
• Language: Python
• Homepage:
• Size: 3.71 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Predict Customer Churn with Clean Code

## Description

This project is about Clean Code Principles. 

The problem is to predict credit card customers that are most likely to churn using clean code best practices.

## Prerequisites

Python and Jupyter Notebook are required.

Also a Linux environment may be needed within windows through WSL.

## Dependencies

- sklearn

- numpy

- pandas

- matplotlib

- seaborn

- pytest

## Installation

Use the package manager [conda](https://docs.conda.io/en/latest/) to install the dependencies from the ```conda.yml```

```bash

conda env create -f conda.yml

```

## Usage

The main script to run using the following command.

```bash

python churn_library.py

``` 

which will generate

- EDA plots in the directory ```./images/EDA/```

- Model metrics plots in the directory ```./images/results/```

- Saved model pickle files in the directory ```./models/```

- A log file ```./log/churn_library.log``` 

The tests script can be used with the following command which will generate a log file ```./churn_script_logging_and_tests.log``` 

```bash

python churn_script_logging_and_tests.py

```

## License

Distributed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0.txt) License. See ```LICENSE``` for more information.