Machine Learning based Loan Eligibility Prediction using Random Forest Model

When one or more people, organizations, or other entities lend money to other people, organizations, or entities, it is known as a loan. The recipient (that is the borrower) takes on a debt for which he or she is normally accountable for interest payments until the loan is repaid. The major goal of this proposed model is to ensure that an individual, institution, or organization seeking for a loan is properly verified before granting them the loan they require. Before authorizing a loan for any individual or business several factors must be considered. That including gender, education, and the number of dependents. The goal of proposed model is to automate the method, which will save time and energy while improving the efficiency of the process. This particular process input is having two different kind of data set. First one is train data set and second set is test data set. The first date set that is train data set is generally used to train and assess the machine learning model accuracy. The loan eligibility predictions are generated using the test data set. To forecast loan eligibility and train this random forest, machine learning method called Random Forest. The proposed random forest model is providing higher accuracy level. This model is providing 28 % higher accuracy level compare to regular prediction.