PREDICTIVE MODELING FOR LOAN PREDICTION

Abstract

Technology has boosted the existence of human kind the quality of life they live.
Every day we are planning to create something new and different. We have a solution for
every other problem we have machines to support our lives and make us somewhat complete
in the banking sector candidate gets proofs/ backup before approval of the loan amount. The
application approved or not approved depends upon the historical data of the candidate by the
system. Every day lots of people applying for the loan in the banking sector but Bank would
have limited funds. In this case, the right prediction would be very beneficial using some
classes-function algorithm. An example the logistic regression, random forest classifier,
support vector machine classifier, etc. A Bank's profit and loss depend on the amount of the
loans that is whether the Client or customer is paying back the loan. Recovery of loans is the
most important for the banking sector. The improvement process plays an important role in
the banking sector. The historical data of candidates was used to build a machine learning
model using different classification algorithms. The main objective of this paper is to predict
whether a new applicant granted the loan or not using machine learning models trained on the
historical data set.