Bank Loan Default Prediction Model Capstone Project - Management Assignment Help

Assignment Task:

Task:

Many banks believed lending to individuals is the risk-free given thy are better placed with credit scores and sometimes the loans are backed by collateral. But recently the banking system has witnessed an increase in the loan default i.e. the borrower is not able to pay back the instalment on time. These loan defaults directly impact the revenues of a banking system.

Now a days, banks are scrutinizing each loan application to identify potential loan default cases so that they can predict which client is going to default the loan repayment and at which step.

Based upon the given data from a bank, build a model to predict default loan that will help the bank to take required actions.

Model Building

We had created a dataset with the important 13 variables from Notes1 and the target variable. We then try various model building by splitting the data in 70:30 ratio (train:test) and check their performance. We have even tried to SMOTE the data and build logistic regression model but there was no significant difference in performance metrics namely: accuracy, sensitivity and specificity. Hence, we have built various models on actual data only without any treatment for imbalance. Please refer R-Code for Source Code of model with SMOTE data.

3.1 Logistic Regression

Logistic regression is part of the supervised learning. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.. There is significant change in log likelihood from the base model. Also based on the p-value we can reject the null hypothesis. Thus the model is valid.

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