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An Initial Survey on House Loan prediction

Abhishek ., Umesh Shantesh Hakkapakki, Pranav Jagan Jagan, R. Shruti Sagar, DR. DEEPAK N R, SAYEED .

Abstract


The capability to predict house loan eligibility directly is vital for financial institutions. This study explores how machine knowledge can transform traditional loan evaluation processes by using applicant data analogous as income, credit history, employment status, and property characteristics. By applying robust data preprocessing and advanced algorithms, this disquisition aims to enhance

prophecy delicacy, reduce manual crimes, and streamline the loan blessing process. The findings emphasize the critical part of ensemble styles and point engineering in achieving reliable results, offering perceptivity into optimizing banking operations.


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References


• Smith et al., "Predicting Credit Default with Random Forests," Journal of Financial Technology, 2019.

• Zhang et al., "Gradient Boosting for Predictive Analytics in Finance," Financial Machine Learning Journal, 2021.

• Dataset Source: [Public Financial Dataset Repository].


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