About Project

This team-based project, coded in Python, determines if a loan applicant is eligible for a loan based on answers to questions. There are two sets of questions, a fast-track set of 3 questions and a larger set of 7 more in-depth questions. Additionally, if the user is determined to be ineligible for the loan based on the fast-track questions, they can move into the in-depth. Based on the user's inputted answers, the program uses truth tables and boolean logic/boolean equations to determine the applicant's eligibility.

This project has some real-world relevance because, in today's world, businesses must use good decision-making if they want to have any hope of being financially successful. This is especially true for a bank or loan agency, as decision-making when giving out loans is an essential task that can lead to profits or losses. The loan applicant should also be judged fairly when giving this loan, regardless of economic class. This Python project, utilizing truth tables and boolean logic, accurately determines a loan applicant's eligibility, something that is more important than ever for banks and loan agencies to be successful in today's financial climate.

This program uses the ideas of truth tables and boolean logic to generate an eligibility determination based on the applicants' responses to one or both of two question sets. One question set, the "fast-track" question set, contains three questions. If the user answers "yes" to all three of these questions, they are automatically granted the loan. If they are determined to be ineligible for the loan after this round of questions, they are recommended to move on to a more in-depth question set containing 7 questions, all of which are weighted differently to determine applicant eligibility. The user can choose to use one or both question sets, which helps to save time and allow for a more fair process. The program contains a variety of features to enhance the user experience including a save feature, a feature that quickly restarts the program, and more.

Here is a sample run of the program in action: