Credit Card Approval Prediction | Credit Risk Assessment

Overview

Our banking client has tasked us to assess their clients risk in order to automate the credit card application procedures.

We have been provided with two datasets:

1. application_record.csv : Dataset of applications records
2. credit_record.csv : Credit records of existing clients of the bank

Our goal is to develop a machine learning model in order to assess whether an applicant qualifies for a credit card, and therefore whether their application is going to be approved or desclined. To do that we need to assess whether a client holds high or low risk. Notably, the criteria for categorizing an applicant as 'approved' or 'declined' is unspecified in our data.

Table of Contents

• Files
• Datasets
• Installation
• Contact Information
• License

Files

• credit_card_approval_prediction.ipynb : EDA, model training and evaluation.
• data : Contains the datasets.
• requirements.txt : Dependencies to execute the notebook cells.

Datasets

The datasets and extensive documentation can be found here.

Installation

1. Clone this repository:

git clone https://github.com/stefsyrsiri/credit-card-approval-predition

2. Change to the project directory:

cd credit-card-approval-predition

3. Install the required packages:

pip install -r requirements.txt

Contact Information

If you have any questions, suggestions, or just want to connect, message me on LinkedIn 😸

License

Distributed under the MIT License