CLASSIFICATION OF BANK MARKETING DATA USING SUPPORT VECTOR MACHINE

Abstract

Nowadays, banking system plays an important role of financial sectors all over the world. The more accurate predictive modeling system is required for their services or products in the banking industry. Bank workers can make those predictive models with manually, but this process takes long time and lots of man-hours. For these reasons, machine learning techniques are useful to predict the outcomes with huge amounts of data. Classification is an important technique to analyze and to predict the data. This system will implement the classification of bank marketing data using support vector machine (SVM) to predict the probability of the customers’ subscription to the term deposit whether subscribe or not. Support Vector Machine (SVM) is a supervised learning model used for classification and prediction of data. The purpose of this system is to predict the customers' response to the term 'deposit' using bank marketing data. The precision, recall, and F-Measure confusion matrix is used to gauge the system's correctness. In the first experiment when the training data is used, the accuracy without feature engineering is 86%, the accuracy with feature engineering is 83% and the accuracy with feature engineering of Correlation Matrix and Principal Component Analysis gets 96%. In the second experiment which is used the testing data, the accuracy without feature engineering gets 85%, the accuracy with feature engineering before using PCA is 83% and the accuracy after using PCA is 95%. The system shows the best results in both training data and testing data after using the Principal Component Analysis.