-> 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.
-> Here we have a data set with following features, we need to go through each and every variable of it to understand and for better functioning.
-> Size of Dataset Provided: -Rows : 850, Columns : 9 (includes 1 dependent variable)
-> Missing Values: Yes
->Outliers Presented: Yes
->Below mentioned is a list of all the variable names and what they stand for:
Attributes: ·
• Age : Age of each customer
• Education : Education categories.
• Employment : Employment status Corresponds to job status and being converted to numeric format.
• Address : Geographic area -Converted to numeric values.
• Income : Gross Income of each customer
• debtinc : Individual’s debt payment to his or her gross income.
• creddebt : debt-to-credit ratio is a measurement of how much you owe your creditors as a percentage of your available credit (credit limits)
• othdebt : Any other debts
-> For Further details Please refer Loan Project Report.docx