Predict loan approval by using different variable selection methods

This case study aims to give you an idea of applying EDA in a real business scenario. The loan providing companies find it hard to give loans to people due to their insufficient or nonexistent credit history. Because of that, some consumers use it to their advantage by becoming defaulters.

Decision Tree algorithm used to predict defaulters of bank loan customers.

Perform Exploratory Data Analysis(EDA) on loan applications to understand how various client attributes (like marital status, education, occupation, etc.) influence the tendency of default.

Assessing multiple model iterations to find the best predictor to whether or not a person will default on their bank loan. The data comes from the Machine Learning Repository - https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients

Project on Bank Loan Default Prediction