Linear Regression Algorithm Based Price Prediction of Car and Accuracy Comparison with Support Vector Machine Algorithm

Abstract

The aim of the study is to use Linear Regression (LR) algorithm based price prediction of car price and accuracy comparison with support vector machine (SVM) classification algorithm. Materials and methods: LR (N=205) and SVM algorithm (N=205) are applied for car price prediction as a mechanism. The accuracy and prediction of the classifiers was evaluated and recorded with G power 80% and alpha value 0.05. Results: The SVM produces 89% accuracy in predicting the car price on the sample dataset and the LR predicts the accuracy at the rate 91.7%. LR algorithm based accuracy appears (significant 0.563) to be better than SVM algorithm for car price prediction. Conclusion: The accuracy performance parameter of the LR algorithm appears to be better than the SVM algorithm.