Bank-Loan-Default-Case

Problem Statement - The loan default dataset has 8 variables and 850 records, each record being loan default status for each customer. Each Applicant was rated as “Defaulted” or “Not-Defaulted”. New applicants for loan application can also be evaluated on these 8 predictor variables and classified as a default or non-default based on predictor variables.We need to identify which customer is "defaulted" and "Not-defaulted"

Number of attributes:

1. Age - Age of each customer - Numerical
2. Education - Education categories - Categorical
3. Employment - Employment status Corresponds to job status and being converted to numeric format - Numerical
4. Address Geographic area Converted to numeric values - Numerical
5. Income - Gross Income of each customer - Numerical
6. debtinc - Individual’s debt payment to his or her gross income - Numerical
7. creddebt - debt-to-credit ratio is a measurement of how much you owe your creditors as a percentage of your available credit (credit limits) - Numerical
8. othdebt - Any other debts - Numerical