Credit Assessment and Recommendation System (CARS) using Naive Bayesian Algorithm

With the increasing demand for credit services to fuel economic growth, traditional credit assessment methods, relying on outdated regulations and manual evaluations, hinder efficiency. The proposed Credit Assessment and Recommendation System aims to revolutionize this process by using a Naïve Bayes Classifier to swiftly and accurately determine credit eligibility. By analyzing customers' biographic, demographic, and historical data, the system can predict approval outcomes and provide actionable recommendations. This approach promises to streamline the credit application process, reduce risks associated with manual assessments, and achieve a prediction accuracy of 90%.