Prediction of Loan Eligibility Approval using Machine Learning

This research explores the application of various machine learning algorithms, including Decision Trees, Random Forest, Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes, to predict loan eligibility approval. In the financial sector, accurately determining whether an individual is eligible for a loan is crucial for risk management and ensuring fair lending practices. The study employs a diverse set of algorithms to assess their efficacy in classifying loan applicants as either eligible or not eligible based on historical data and relevant features. Decision Trees provide interpretability, Random Forest enhances robustness through ensemble learning, Logistic Regression models the probability of approval, SVM optimizes decision boundaries, KNN relies on neighborhood patterns, and Naïve Bayes assumes independence between features. The evaluation of these algorithms involves performance metrics such as accuracy, precision, recall, and F1-score, considering the delicate balance between false positives and false negatives. The research aims to identify the most suitable algorithm or combination of algorithms for accurate and efficient loan eligibility predictions, contributing valuable insights to the financial industry for automated decision-making processes. The findings of this study have the potential to enhance risk assessment procedures, reduce manual workload, and streamline loan approval processes, ultimately benefiting both financial institutions and loan applicants.

Keywords: Decision tree, Random forest, Logistic regression, SVM(support vector machine),KNN( K-nearest neighbors), Naïve bayes and Machine learning techniques.