The West Bengal Student Credit Card (WBSCC) program was introduced by the West Bengal government on June 30, 2021. Students from West Bengal, India, are eligible to apply for loans up to under the WBSCC scheme 10 lakhs to finish their education starting in class 10 and continuing until they turn 40 both domestically and overseas (Ghara TK in IOSR Journal Of Humanities And Social Science (IOSR-JHSS) 27(1). e-ISSN: 2279-0837, p-ISSN: 2279-0845). In this study, we are trying to find the possibility of getting the shame of a new student who is going to applying for the WBSCC using data science. In this paper, we collect some WBSCC data from different colleges, and after that, we use decision tree (DT) and random forest (RF) classifiers to find this prediction. In our work, we also use cross-validation and optimization parameter to get the batter result. Here, we are finding the accuracy, macro-recall, macro-precision, and macro-F1-Score to identify which cases we are gating the high result. After analyzing all the data, finally we find the best result in terms of accuracy using RF classifiers with optimization of some parameters using the grid search technique.