Chapter: 1
INTRODUCTION
For banking institutions, loan approval is an important step. The system either
approved or denied the loan applications. An important factor in a bank's financial
statements is loan recovery. Bankers must examine a person's data before issuing a
loan because bank data is expanding in banks at such a quick rate today. It is very
challenging to forecast whether the borrower will be able to repay the loan.
Machine learning (ML) methodologies are quite useful for predicting outcomes
when working with enormous amounts of data. Data learning from its own
experiences, as well as data prediction and decision-making, are specifically
assisted by machine learning. Our research uses the Python programming language
because it already has all of the necessary tools and libraries. One of the most
popular and widely used languages for machine learning and artificial intelligence
is Python. Data has revolutionized computer science as the most valuable resource
in the world. Numerous data analysis solutions have been made possible by
machine learning algorithms. To forecast if a customer's loan application will be
approved, we would employ five machine learning algorithms: the Decision Tree
algorithm, Random Forest algorithm, Logistic Regression algorithm, SVM
algorithm, and K Neighbors algorithm. Our main objective is to apply machine
learning ideas to determine a customer's loan status and forecast a prompt, exact
result that helps the lender analyze the situation, improve services, and reduce risk
by choosing the right candidate, saving the lender time and money. Additionally,
we would evaluate multiple machine learning algorithms and choose the top one.
The breakdown of the paper's structure is as follows: In Section II, a summary of
pertinent literature reviews on research publications on loan prediction is provided.
Section III contains a Data Description section. Section IV discusses the technique
used in the study to produce its findings. The section V contains the study's
findings. Section VI concludes the essay and discusses the project's ongoing
activities.
