Predicting Home Loan Approvals Using Random Forest Classifiers: A Comprehensive Machine Learning Approach

Machine learning models have become essential in the shifting topography of the financial sector for enhancing decision-making procedures especially in loan approval and credit risk assessment. This work investigates the use of Random Forest classifiers in estimating house loan approvals using a big dataset encompassing numerous demographic, financial, and credit-related factors of applicants. The task consists on meticulous data preparation, feature engineering, and model validation to provide robust and consistent forecasts. Random Forest models demonstrate high performance in managing missing values and handling challenging datasets, so this reveals that they generate good accuracy and interpretability. We show the advantages of Random Forests—especially their ensemble character—which reduces overfitting and improves generalization by rigorous analysis and comparison with other machine learning methods. The results show how Random Forest classifiers could improve lending policies and risk management in financial companies as well as provide interesting study of the factors influencing home loan approvals. This paper adds to the growing corpus of knowledge on the practical applications of machine learning in finance by offering a consistent approach for project home loan approvals.