Loan Eligibility Prediction using Machine Learning based on Personal Information

Banks serves the basic necessities of everyone next to hospitals and schools. People reach out to banks for various purposes. But one of the most common services offered by banks is loans. However, many common people are not completely aware of the banking procedures and eligibility criteria for loans. This study aims to develop a Machine Learning (ML) model which is capable of predicting whether the person is eligible for a health loan or not by analyzing some basic values entered by the user. For this process, a dataset consisting of all necessary parameters for a loan application is collected from Kaggle. The collected dataset is then preprocessed by two methods namely the null value elimination method and encoding. Simultaneously, three ML models were developed using three different algorithms. They are the Random Forest (RF), Naive Bayes (NB), and Linear Regression (LR). The preprocessed data will next be used to train the models. Following that, a comparison of a few parameters will be used to assess the models' effectiveness. The results of the analysis prove that the RF algorithm is the best in terms of both accuracy and error. The accuracy of the RF algorithm is 91% and it also predicts loan eligibility with lesser error values. The LR model has the lowest accuracy values and the highest error value making it the least efficient algorithm that can be used in loan prediction.