This project demonstrates the process of calculating credit scores and segmenting customers based on their credit profiles. It is designed to help financial institutions assess the creditworthiness of individuals or businesses, enabling informed decisions about lending, interest rates, and credit limits.
Credit scoring involves evaluating various factors like payment history, credit utilization ratio, and employment status to determine a credit score for each individual. Customers are then segmented into distinct groups based on their credit scores to manage credit risk effectively.
- Data Preprocessing: Handles real-world data containing information like age, marital status, education level, and employment status.
- Credit Score Calculation: Implements the FICO scoring method to compute credit scores based on various weighted factors.
- Visualization:
- Distribution of credit utilization ratio and loan amounts.
- Correlation heatmap of numerical features.
- Segmentation: Groups customers into different credit tiers based on predefined credit score thresholds.
The dataset includes features such as:
- Demographics: Age, Gender, Marital Status, Education Level.
- Financial Behavior: Payment History, Credit Utilization Ratio, Number of Credit Accounts.
- Loan Details: Loan Amount, Interest Rate, Loan Term, Type of Loan.
- Clone this repository:
git clone https://github.com/your-username/credit-scoring-segmentation.git