Before making a loan to a person, the organisation should examine their creditworthiness to reduce the possibility of risk to their credit. Banks, credit card firms, insurance providers, property managers, governments, and mortgage lenders are some examples of these organisations. A person's three-digit credit score is all it takes to tell lenders whether they can repay a loan within a certain period of time. The better the borrower seems to potential lenders, the higher the credit score. A person's credit history, including the number of open accounts, overall debt levels, payment history, and other characteristics, is used to calculate their credit score. The credit score of an individual can be predicted using a variety of machine learning algorithms. However, due to its desirable qualities, including clarity and robustness, logistic regression is thought to be the most often used model of all. In this study, we employ logistic regression to build a credit-scoring model that can determine whether or not a consumer is reliable based on his credit score.