Loan Default Dataset

Description:

Banks earn a major revenue from lending loans. But it is often associated with risk. The borrower's may default on the loan. To mitigate this issue, the banks have decided to use Machine Learning to overcome this issue. They have collected past data on the loan borrowers & would like you to develop a strong ML Model to classify if any new borrower is likely to default or not.

The dataset is enormous & consists of multiple deteministic factors like borrowe's income, gender, loan pupose etc. The dataset is subject to strong multicollinearity & empty values. Can you overcome these factors & build a strong classifier to predict defaulters?

Acknowledgements:

This dataset has been referred from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build classification model to predict weather the loan borrower will default or not.
• Also fine-tune the hyperparameters & compare the evaluation metrics of vaious classification algorithms.