TY  - MANSCPT
AU  - Nik Nur Ain Binti Nik Jid,
TI  - Increase the efficiency of loan prediction using machine  learning approach
SN  - THE0010021 (Local)
PY  - 2023///
CY  - Kuantan, Pahang
PB  - UMPSA
KW  - Centre for Mathematical Sciences
KW  - Dissertations
KW  - Universities and colleges
N1  - Centre for Mathematical Sciences; Bachelor of Applied Science in Data Analytics with Honours -- Universiti Malaysia Pahang – 2023; Includes bibliographical references
N2  - The 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
ER  -