Loan Defaulter Prediction System using Machine Learning

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creativework.keywordsLoan Defaulter,banking sector,Support Vector Machine
dc.contributor.advisorA.K.M. Bahalul Haque
dc.contributor.authorMushayev Masrur
dc.contributor.authorIbrahim Khalil
dc.contributor.authorMd. Tousif Rob Chowdhury
dc.contributor.authorMd. Muhibul Hasan
dc.contributor.id1511122042
dc.contributor.id1510831042
dc.contributor.id1511387642
dc.contributor.id1531022642
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2024-05-14
dc.date.accessioned2024-05-14T06:56:24Z
dc.date.available2024-05-14T06:56:24Z
dc.date.issued2019
dc.description.abstractWith the growth in banking sector lots of people and companies are applying for bank loans but the banks have their limited assets which they have to grant which will be a safer option for the banks is a difficult process. So, in this paper we try to reduce these risk factor/s of the banks to select a particular person or a company for providing loans. This is done by analyzing the data of the previous records of the people to whom the loan was granted before and on the basis of these records/experiences the system will be trained using the machine learning model which gives the most accurate result. The main objective of this project is to predict whether assigning the loan to particular person/company will be safe or not. This will be done by finding out the chances of a loan seeker being a defaulter or not. To achieve the maximum limit of the goal, applying classification models is the most efficient way. In this particular research, the three most popular and useful models; Logistic Regression, Random Forest, Decision Tree and Support Vector Machine are being implemented. Simultaneously, users who are also the bank personnel, can also have an access to a User Interface which is discussed in this paper as well. The UI will allow the users to input data for new loan seekers, so that the banks can predict the output for a loan seeker being defaulter or not in the form of binary output.
dc.description.degreeUndergraduate
dc.identifier.cd600000543
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/700
dc.language.isoen
dc.publisherNorth South University
dc.rights© NSU Library
dc.titleLoan Defaulter Prediction System using Machine Learning
dc.typeProject
oaire.citation.endPage58
oaire.citation.startPage1
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