Prediction of Customer Switching Using Support Vector Machine Method

Abstract

Several studies on predicting customer switching focus on the telecommunications industry and online stores. This research aims to predict customer switching to get the best results; customers are the most critical mass; some companies must provide satisfying services so customer flow decreases. The support vector machine (SVM) method uses machine learning to find a hyperplane based on the SRM principle. A hyperplane is a decision boundary that helps classify data points. SVM stands out for its ability to take input data and make predictions based on its characteristics. This study uses data from Kaggle, structured it, cleaned it, identified patterns and inconsistencies (such as skewness, outliers, and missing values), and built and validated hypotheses. From the data processing, the plot shows the imbalance of data classes between churners and non-churners. This research applies several models where the most significant or best performance value is in the SVM model of 0.7996. The Neural Network model can be trained with better patterns to detect data and achieve high accuracy.