Predicting acceptance of the bank loan offers by using support vector machines

Banks frequently promote personal loan offers to their customers, yet it may be hard to find out which customers are most likely to accept deals. Banks can lower operating expenses and enhance marketing tactics by forecasting client responses. This paper proposes a machine learning method that makes use of the Support Vector Machine (SVM) algorithm to forecast whether bank loan offers will be accepted. The Bank Personal Loan Modelling dataset, which includes financial and demographic data including age, income, family size, education level, and credit card use, is utilized to train the model. Techniques for feature selection and data pretreatment in order are used prior to model training. The outcomes show that SVM is capable of accurately recognizing clients who are most likely to accept loan offers.