LOAN STATUS PREDICTION USING DECISION TREE CLASSIFIER
Abstract
This paper investigates the effectiveness of the Decision Tree Classifier in predicting loan status, a critical task in the financial sector. The study utilizes a dataset containing various attributes of loan applicants such as income, credit score, employment status, and loan amount. The dataset is preprocessed to handle missing values and categorical variables. Feature importance is analyzed to understand the key factors influencing loan approval decisions. A Decision Tree Classifier model is trained and evaluated using performance metrics such as accuracy, precision, recall, and F1-score. The results demonstrate the feasibility of using Decision Trees for loan status prediction and provide insights into the decision-making process of loan approval.
Keywords
Loan Status Prediction, Decision Tree Classifier, Credit Risk Assessment, Feature Importance, Model Evaluation
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References
Sheikh MA, Goel AK, Kumar T. An approach for prediction of loan approval using machine learning algorithm. Paper presented at: 2020
Madaan M, Kumar A, Keshri C, Jain R, Nagrath P. Loan default prediction using decision trees and random forest: a comparative study.
Gautam K, Singh AP, Tyagi K, Kumar MS. Loan prediction using decision tree and Random Forest. 2008.
Dansana D, Patro SGK, Mishra BK, Prasad V, Razak A, Wodajo AW. Analyzing the impact of loan features on bank loan prediction using Random Forest algorithm. Engineering Reports. 2024; 6(2):e12707. doi: 10.1002/eng2.12707
DOI: https://doi.org/10.30591/polektro.v12i3.6591
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