Loan Distribution and Prediction System Using Supervised Machine Learning

Author Name : Mohammed Abdul Kareem Shaik, Gandla Vishnu Kailash, Uppara Nithin

ABSTRACT

In the contemporary era, individuals increasingly rely on bank loans to fulfill their aspirations, but the associated loan fees have surged rapidly in recent years. Loan approval involves inherent risks, despite meticulous analysis by banking officials. Recognizing the imperative for a more reliable and less risky loan approval process, the integration of Computer Science and Artificial Intelligence (AI) emerges as a viable solution. Through data science and machine learning algorithms such as Decision Trees, Logistic Regression, and Random Forest a predictive system is created. This system efficiently handles vast amounts of data, leveraging a training dataset to accurately determine the suitability of a loan applicant. By examining fundamental applicant information like profession, age, gender, and marital status, the system employs visualization and machine learning algorithms to streamline and enhance the loan approval decision-making process.