You are requested to create an India credit risk(default) model, using the data provided in the spreadsheet raw-data.xlsx, and validate it on validation_data.xlsx. Please use the logistic regression framework to develop the credit default model.
Data description – Default Risk Prediction. After removing variables for multicollinearity, we should try to take at least one variable for creating the model from each of the 4 factors namely – 1) Profitability2) Leverage3) Liquidity4) Company’s sizeclearly bifurcate all the variables in different buckets.Creation of new variables – This is an important step in the project as the company which is the biggest in size, will also have bigger asset size, cash flows etc. (Hint: We need to think in terms of ratios – Equity to asset ratio, debt to equity ratio etc)Dependent variable – We need to create a default variable which should take the value of 1 when net worth is negative & 0 when net worth is positive.Validation Dataset – We need to build the model on raw dataset and check the model performance measures on validation dataset.
Please find attached the files to be referred.
Missing Value Treatment
New Variables Creation (One ration for profitability, leverage, liquidity and company’s size each )
Check for multicollinearity
Univariate & bivariate analysis
Build Logistic Regression Model on most important variables
Analyze coefficient & their signs
Model Performance Measures
Predict accuracy of model on dev and validation datasets
Sort the data in descending order based on probability of default and then divide into 10 dociles based on probability & check how well the model has performed
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