Predicting Credit Card Approvals

Foreword

This project demonstrates a logistic regression classification model to automate credit card approvals.
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Table of Contents

1. About The Project
2. Getting Started
   • Installation
   • Dependencies
3. License

About The Project

Predicting Credit Card Approvals

Banks receive a lot of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low income levels, or too many inquiries on an individual's credit report, for example. Manually analyzing these applications is mundane, error-prone, and time-consuming (and time is money!). Luckily, this task can be automated with the power of machine learning and pretty much every commercial bank does so nowadays. In this notebook, an automatic credit card approval predictor is built using machine learning techniques.

Getting Started

To recreate this project locally, you can clone this repository via HTTPS as follows.

git clone https://github.com/jgome284/Predicting-Credit-Card-Approvals.git 

Environment

Virtual environments are a great way to keep your system-wide Python installation clean and organized. Each virtual environment is isolated from the others, so changes you make to the packages in one virtual environment will not affect the packages in another virtual environment. This can help to prevent conflicts between packages that are used by different projects.

Create

You can create a virtual environment named my_env to manage this projects dependencies, for example. To do so, run the following command:

python3 -m venv my_env

Activate

To activate the virtual environment, run the following command:

source my_env/bin/activate

Deactivate

To deactivate the virtual environment, run the following command:

deactivate

Dependencies

Python version 3.9.6 was used to run this analysis. To install additional dependencies, run the following command:

pip install -r requirements.txt

Best practice is to install these dependencies into an activated virtual environment. pip should be smart enough to handle dependencies between all packages required during installation.

License

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