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.