A Machine Learning Approach to Accurate and Efficient Loan Status Prediction
Dr. Sunil Bhutada 1,M.Azad 2,K.Maniteja 3, K.Venkatsai 4
1 Head of the Department and Professor, IT Department, Sreenidhi Institute of Science and Technology, Yamnampet, Hyderabad :sunilb@sreenidhi.edu.in
2 B. Tech 4th Year, IT Department, Sreenidhi Institute of Science and Technology, Yamnampet, Hyderabad : Mandariazad6666@gmail.com
3 B. Tech 4th Year, IT Department, Sreenidhi Institute of Science and Technology, Yamnampet, Hyderabad :korthiwadamaniteja@gmail.com
4 B. Tech 4th Year, IT Department, Sreenidhi Institute of Science and Technology, Yamnampet, Hyderabad : kandukurivenkatsai11@gmail.com
ABSTRACT
Predicting the status of a loan application is a crucial task for financial institutions, as it helps them in making informed lending decisions and managing risk. Traditional methods for loan status prediction involve the manual analysis of a large number of variables and have proven to be time-consuming and error-prone. With the increasing availability of digital data, machine learning techniques have the potential to significantly improve the accuracy and efficiency of loan status prediction. In this project, we propose to develop a machine learning model for predicting the status of a loan application using a dataset of past loan records. Our model will be trained on a variety of features, including borrower's credit score, loan amount, employment history, and financial statements. We will compare the performance of different machine learning algorithms and select the one that provides the highest prediction accuracy. The results of our model will be evaluated using a set of standard metrics and will be compared with those obtained from traditional methods. The proposed model has the potential to significantly improve the efficiency and accuracy of loan status prediction, and can be used by financial institutions to make more informed lending decisions.
Keywords: Customer Analytics, logistic regression, decision tree , random forest, support vector machine , Evaluation metrics
