Problem Statement
Loan Eligibility Prediction using Gradient Boosting Classifier
The primary product that banks offer is loans. The interest on the loan serves as the primary source of income. After a thorough verification and validation process, the loan companies grant a loan. They are still unsure, though, if the applicant would be able to return the money without incident.
PS Number: PSAIML006
Domain Bucket: Artificial Intelligence
Category: Software
Dataset : NA
As part of the solution, you will create a classification model to determine whether or not a loan applicant will be approved. You will do this by analysing the applicant’s prior performance and credit score in order to try and forecast whether or not a loan will be granted.
Background of the Problem
Loans are the core business of banks. The main profit comes directly from the loan’s interest. The loan companies grant a loan after an intensive process of verification and validation. However, they still don’t have assurance if the applicant is able to repay the loan with no difficulties.
Objective
The loan applications which come into their various branches are processed manually. The decision whether to grant a loan or not is subjective and due to a lot of applications coming in, it is getting harder for them to decide the loan grant status. Thus, they want to build an automated machine learning solution which will look at different factors and decide whether to grant a loan or not to the respective individual.
Summary
Through the Solution, you will build a classification model as we have to predict if an applicant should get a loan or not, you will look at various factors of the applicant like credit score, past history and from those we will try to predict the loan granting status. You will also cleanse the data and fill in the missing values so that our ML model performs as expected. Thus you will be giving out a probability score along with Loan Granted or Loan Refused output from the model.
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