Linear Regression vs. Support Vector Machine: A Formal Enhancement in Online Purchase utilizing Novel Customer Feedback Segmentation

Abstract

Aim: The aim of the research is to improve the customer segmentation in Online purchases using Novel Customer Feedback. Materials and methods: The Categorizing is performed by adopting a sample size of n=10 in Linear Regression and sample size n=10 in Support Vector Machine algorithms with a sample size = 10 was iterated 20 times for efficient and accurate analysis on labeled images with G power in 80% and threshold 0.05%, CI 95% mean and standard deviation. Results and Discussion: The analysis of the results shows that the Linear Regression has a high accuracy of (88.56%) in comparison with the Support Vector Machine (81.30%). There is a statistically significant difference between the study groups with (p<0.001). Conclusion: Prediction in customer segmentation in E-Commerce shows that the Linear Regression appears to generate better accuracy than the Support Vector Machine algorithm.