Comparison of linear regression and support vector machine for the estimation of demand and supply of workers for timely completion of public sector industry construction

This study compares support vector machines (SVM) with a novel kernel trick to linear regression to estimate worker supply and demand for timely completion of public sector industry construction. Materials and methods: Compare the results of the two groups of models in this study. Linear regression comes first, followed by support vector machines. The developed regression models were validated using four out-of-sample projects, several diagnostic tests, and tests for timely completion of public sector industry building. In this study, there are two groups, each having a sample size of 52. Results: The experiments are conducted in the laboratory and we report an average value of 2.97, for an ensemble classifier which combines two approaches. From the results it has been observed that Support vector machines (90.06%) have more Accuracy than Linear Regression (64.02%) and obtained a significance of P = 0.001 (P < 0.05) two-tailed based independent sample T test conducted on both the groups. The suggested SVM model offers a method for predicting the labor demand for construction in the coming years. The quality of the daily records that were obtained from the workers will determine how accurate the model is.