jpcadena / logistic-regression-course

Logistic Regression Course for Customer Churn.

logistic-regression-course

Logistic Regression Course

Description for Logistic Regression Course
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Contributing
5. License
6. Contact

About the project

This project is about Logistic Regression...

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Built with

• 

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Getting started

Prerequisites

• Python 3.10+

Installation

1. Clone the repository

   git clone https://github.com/jpcadena/logistic-regression-course.git
   

2. Change the directory to root project

   cd logistic-regression-course
   

3. Create a virtual environment venv

   python3 -m venv venv
   

4. Activate environment in Windows

   .\venv\Scripts\activate
   

5. Or with Unix/Mac OS X

   source venv/bin/activate
   

6. Install requirements with PIP

   pip install -r requirements.txt
   

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Usage

1. If found sample.env, rename it with to .env.
2. Replace your credentials into the .env file.
3. Execute with console

   python main.py
   

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Contributing

If you have a suggestion that would make this better, please fork the repo and create a pull request.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Use docstrings with reStructuredText format by adding triple double quotes """ after function definition.
Add a brief function description, also for the parameters including the return value and its corresponding data type.
Please use linting to check your code quality following PEP 8.
Check documentation for Visual Studio Code or Jetbrains Pycharm.
Recommended plugin for autocompletion: Tabnine

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License

Distributed under the MIT License.

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Contact

LinkedIn: Juan Pablo Cadena Aguilar

E-mail: Juan Pablo Cadena Aguilar

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