A project to predict if credit (loan) would be approved to a customer, given a set of features such as the customer's age, income, credit intent, credit history, credit default history.
A machine learning approach using random forest classifier was taken to predict the approval status of customers. Data cleaning and preprocessing was carried, particularly SMOTE technique was used to handle the class imbalance of the dataset