Bank Loan Personal Modelling Using Classification Algorithms of Machine Learning

Bank Loan is a major burden worldwide in the 21st century. Human services are overpowering national and corporate spending plans because of asymptomatic loans including education loans, business loans, etc. Consequently, there is an urgent requirement for early solution of such loans balances and finding the persons who are capable of buying the personal loan. The information which is collected is by data analysis of banks is utilizing by applying different blends of calculations and algorithms for the early-stage prediction of whether the person is able to pay the loan or the customer is liable to take personal loan. Machine Learning is one of the slanting innovations utilized in numerical circles far and wide in many fields. In this research, we compared the accuracy of machine learning algorithms that could be used for predictive analysis of loan clearance and predicting overall risks associated with it. The proposed experiment is based on a standard machine learning algorithms such as Logistic Regression, K-Nearest Neighbors (KNN), Random Forest, Support Vector Machine (SVM), Decision Tree and Gradient Boosting. Most of the entities in this world are related in one or another way, at times finding a relationship between entities can help you make valuable decisions. Likewise, I will attempt to utilize this information as a model that predicts the person is able to pay the loan amount or not. Moreover, the data analysis is carried out in Python using Google Colab in order to validate the accuracy of all the Algorithms.