Abstract:
With the increase in the number of credit applicants, effective and efficient classification methods are required in order to the deliver appropriate result of credit eligibility. The performance of credit classifiers usually depends on the facilitators and attributes used as the parameters to determine the eligibility. In this research, random forest as the machine learning algorithm is proposed to solve the problem by defining the attributes with chosen dataset that is related to credit eligibility which will go beyond the data mining process, trained and tested to find the pattern that will be used in the web-based application to ease the users in knowing their credit eligibility with only filling the data form based on the variables from the dataset. Furthermore, an approach to automatically determine credit eligibility by training an existing dataset in machine learning to find the pattern is proposed. The results determine the credit eligibility by just inputting new data in the web-based application and perform the accuracy of random forest algorithm.
