Loan Eligibility Predictor

EasyChair Preprint 5455

Abstract

With the improvements within the banking sector numerous individuals are applying for a bank loans yet the bank has its limited resources which it has to grant to a small number of people only, so finding out to whom the loan can be permitted which will be a safer option for the bank is a typical process. So in this project, we try to reduce this risk element behind selecting the safe person so as to save lots of bank endeavor and assets. This is done by using data of the preceding records of the people to whom the the loan was granted before and on the basis of these records the machine was trained using the python and ML model which gives the most precise result. The the main objective of this project is to predict whether assigning the loan to a specific person will be safe or not

Keyphrases: Machine Learning Model, data completion, data set, loan, logistic regression, machine learning, prediction, testing, training

@booklet{EasyChair:5455,
  author    = {Pallavi Saindane and Anjali Asrani and Ajay Bathani and Kunal Dongare and Sneha Indulkar},
  title     = {Loan Eligibility Predictor},
  howpublished = {EasyChair Preprint 5455},
  year      = {EasyChair, 2021}}