Credit Scoring and Segmentation

Overview

This project aims to develop a robust credit scoring model that can accurately predict the creditworthiness of individuals by calculating a credit score by FICO formula. Additionally, it segments customers based on their credit scores to enable tailored financial services.

Features

• Feature Engineering: Cleansing and normalization of credit data.

• Calculating Credit Score: Calculating a credit score using fico formula.

  credit_score = (payment_history 0.35) + (credit_utilization_ratio 0.30) + (number_of_credit_accounts 0.15) + (education_level 0.10) + (employment_status * 0.10)

• Segmentation based on credit score: Identification and selection of relevant credit attributes.

• Model Training: Implementation of machine learning algorithms for credit scoring.

• Segmentation: Clustering of customers into segments based on credit score ranges.

• Evaluation: Metrics and methods to assess the performance of the credit scoring model.

Installation

To set up the project environment:

cd credit-scoring-and-segmentation
git clone https://github.com/Gopalkholade/Credit-Scoring-and-Segmentation.git

Usage

Please try running notebook and experimenting😊

Contributing

Contributions to improve the model and segmentation are welcome.