Creation of an India Credit Risk Default Model Using Logistic Regression

Finance and Risk Analytics

The project involved developing a credit risk default model using a given data which had to be checked for outliers, missing values, multicollinearity etc. Univariate and Bivariate Analysis had to be conducted and the model had to be built using Logistic Regression on most important variables. Model Performance Measures were undertaken that included predicting the accuracy of the model on certain datasets.

Skills and Tools

1. Logistic Regression
2. Univariate & Bivariate Analysis
3. Outlier Treatment
4. Model Performance Measures