Although there are various items that banking systems can sell to make money, their primary source of income is their credit card system. Now that the banking industry is doing better, but because banks only have so many assets to lend to, choosing who will be a safer option for the bank and to whom the loan can be provided is usually a procedure. The banking sector still desires a more rigorous predictive modelling framework for a number of issues. Predicting loan defaulters is a difficult task for the banking sector. The loan status, which is the first stage of the loan lending procedure, is one of the quality metrics of the loan. Using machine learning, it is possible to automate the process of determining whether a loan should be authorized or not to the loan asker. This is done in more efficient way by searching through available data for prior loan recipients, after which machine learning methods are used to train the system based on the histories and experiences on available data. There are several ways to analyze the former mentioned issues on loan prediction in accordance with the research conducted by many researchers in this era. In this research paper we basically conducted the exhaustive investigation on DGHI dataset for analyzing the customer eligibility whether he is eligible for loan or not using LRD machine learning algorithms (i.e. Logistic Regression, Random Forest and Decision Trees). The experimental study conducted has been divided into two phases: Training and Testing of the available data. On the basis of investigation conducted we decided to choose Logistic Regression as the best technique for probability of loan prediction for the customer. The results obtained and selection of Logistic Regression as the suitable technique for the given approach has been done on the basis of parameters such as: Loan_id, Gender, Married, Education, Self-employed and so on. For future work it has been decided to improve the accuracy and precision of Logistic Regression.

Keywords : Decision Tree, Logistic Regression, Machine Learning algorithms, Medical Insurance, Prediction, Random Forest.