Credit-Risk-Analysis

This project is all about predicting if someone is eligible for a loan or not, making this process automatically by training a Logistic Regression model by using some variables that a user gives in the loan form. My main task was to clean the data and pre-process it. So that I can use it in my classifier model and it should be able to give higher accuracy of predictions. In this dataset, I have 614 readings and 13 columns'. First I have divided the dataset into dependent and independent variables. After that encoded dependent variable (i.e. 'y'). After that cleaned up all independent variables and then trained the Logistic Regression model. It has 80% accuracy for the training set only and ROC (Receiver Operating Characteristic) AUC (Area Under the Curve) score was 0.70. The same model gives 82% accuracy when predicted for the testing dataset. Finally, for a more optimal model, I made another classifier and trained with Logit function from the statsmodels module with a threshold value of 0.5. I got 83.7% accuracy and the ROC AUC score was 0.725.