A Deep Learning-based Framework for Accurate Loan Prediction: Comparative Insights With Traditional Models

The primary goal of this study is to develop a new approach to loan prediction using Support Vector Machines (SVM) and comprehensive statistical analysis. Improving forecast accuracy and laying a solid foundation for loan eligibility determination are the goals of this undertaking. The goal of this project is to improve the reliability of loan prediction models by leveraging statistical insights and Support Vector Machines (SVMs). The results and borrower profiles were culled from a dataset of loans. After some preliminary processing, we used the Random Forest as a foundation and added a custom Support Vector Machine (SVM). To improve precision, models were trained, performance metrics were examined, and statistical analysis was carried out. The implementation was carried out using Python with scikit-learn and TensorFlow, which guaranteed the preservation of ethical data management practices. The outcome was a statistically significant improvement in loan forecast accuracy between the Random Forest Algorithm and the new Support Vector Machines (SVM). When it comes to forecasting the amount of the loan that the consumer received, the Randomforest algorithm obtains a level of accuracy of 78.86%. A score of 70.73% was achieved by Support Vector Machines (SVM), so this is a significant improvement.