You need to come up with an efficient solution to classify new customers on whether they can be approved for a loan or not. You'll use a series of classification models to figure out the best model and provide a list of creditworthy customers to your manager.
You work for a small bank and are responsible for determining if customers are creditworthy to give a loan to. Your team typically gets 200 loan applications per week and approves them by hand.
Due to a financial scandal that hit a competitive bank last week, you suddenly have an influx of new people applying for loans for your bank instead of the other bank in your city. All of a sudden you have nearly 500 loan applications to process this week!
Your manager sees this new influx as a great opportunity and wants you to figure out how to process all of these loan applications within one week.
Fortunately for you, you just completed a course in classification modeling and know how to systematically evaluate the creditworthiness of these new loan applicants.
For this project, you will analyze the business problem using the Problem Solving Framework and provide a list of creditworthy customers to your manager in the next two days.
Data on all past applications The list of customers that need to be processed in the next few days
Please refer to the following link for the solution. https://github.com/gmalekar/Predicting-Loan-Default/blob/master/Predicting%20Load%20Default%20Submission.pdf