Real-Time Approach To Loan Credit Approval And Credit Risk Analysis Using ML

Abstract

Modern computers' increased computational capabilities and ability to learn on their own have contributed to the emergence of AI. Few ether (dynamic) parameters yield a lot of data. Humans can't explicitly encode all accessible knowledge. Machines that learn this can produce more accurate results/predictions. Time alters environments. Adaptive machines would lessen the need for redesign. Modernized by bots, computers, and automated tools. Massive datasets require automation techniques and computer systems. Machine learning is a branch of AI that educates machines using transactional data.

We employed classification algorithms to develop an ML prediction model. Classification techniques like SVM, Random Forest Classifier, and KNN fit the dataset. During implementation, data patterns must be compared. Regression procedures like linear regression (created from scratch) will improve assignment accuracy (categorical data excluded).