This project will cover the applications of risk analytics in detail in the context of credit risk. The project focuses on all the three forms of analytics. Two datasets have been provided that are to be used.

A$.$ HMEQ Dataset

The data set HMEQ reports characteristics and delinquency information for $5,960$ home equity loans. A home equity loan is a loan where the obligor uses the equity of his or her home as the underlying collateral.

B$.$ Dataset LGD

This dataset comprises of anonymised information of a European bank. It includes $2545$ observations on loans and LGDs$.$

Part $1$ Descriptive Analytics

Carry out an EDA on the HMEQ data set to find insightful patterns in the historical data. Formulate at least two hypotheses and test those using applicable statistical tests and explain your findings.

Study the distribution of the variables in the LGD dataset and enumerate your findings.

Part $2$ Predictive Analytics

For the HMEQ dataset, you are given a target variable BAD $($Please refer the data dictionary for detail$).$ Your task is to apply any binary classification algorithm to the dataset, employing a suitable training and test split.

Use a linear regression model to model the LGD based on the independent variables provided in the dataset.

Part $3$ Prescriptive Analytics

Based on your findings from Part I and Part $2,$ develop some recommendations that you would like to give to the bank. A risk assessment plan will be your key deliverable in this part.