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020			_aTHE0010021 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aPSM .N87 2023 r Bc.
100	0		_aNik Nur Ain Binti Nik Jid, _eauthor.
245	1	0	_aIncrease the efficiency of loan prediction using machine learning approach / _cNik Nur Ain Binti Nik Jid
264		1	_aKuantan, Pahang : _bUMPSA, _c2023
264		4	_c© 2023
300			_axii, 84 pages : _billustrations ; _e1 CD-ROM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aCentre for Mathematical Sciences
502			_aBachelor of Applied Science in Data Analytics with Honours -- Universiti Malaysia Pahang – 2023
504			_aIncludes bibliographical references
520	3		_aThe individual loan approval process is a critical and ongoing issue faced by financial institutions. As the number of loan applications increases year by year, it is crucial for bankers to take the approval process seriously to prevent loan default cases. The goal of this study is to find the best prediction model that can assist financial institutions in overcoming this problem. Loan approval prediction involves determining the likelihood of a loan application being approved or denied by a financial institution. This typically involves analysing the applicant's credit history, income, employment status, and other financial factors. The objective is to reduce risk for the lender and increase the chances of loan approval for the borrower. In this study, machine learning techniques, such as logistic regression, decision trees, support vector machines, random forest, gradient booster, and artificial neural networks, were employed to train models for loan approval prediction using a home loan dataset obtained from Kaggle. The dataset includes variables such as gender, marital status, education, number of dependents, income, loan amount, and credit history. The analysis and modelling were conducted using Python in Google Colaboratory, also used as visualization tools aiding in identifying patterns and trends in the data. The results of the project revealed that the decision trees model exhibited the highest accuracy of 94% and the lowest root mean squared error value of 0.25, making it the most effective model for loan approval prediction. To deploy the prediction model, a web application was developed using Streamlit and Visual Studio Code as the integrated development environment platform. The web application provided an interactive graphical user interface and dashboard for end-users, enabling financial institutions to easily input loan application information and receive predictions on the likelihood of loan approval. By utilizing this prediction model, financial institutions can make more informed lending decisions, ultimately reducing the risk of loan default cases.
610	2	0	_aCentre for Mathematical Sciences _xDissertations
650		0	_aUniversities and colleges _xDissertations
942			_2lcc _cPSM