Predicting CPU Performance Score with Regression Analysis

The purpose of this research is to use regression analysis to predict a CPU's performance score based on its features. CPU performance is incredibly important to evaluate when choosing a computer, along with system configuration and design. Support Vector Regression (SVR), Random Forest Regression (RFR), Multiple Linear Regression (MLR), Gradient Boosting Regression (GBR) and Neural Network Regression (NNR) are used to estimate the CPU's performance score. To test the algorithms, 30 percent of the data set was selected as test data and 70 percent as training data, separated randomly. As a result, the NNR has the highest of the coefficient of determination score which is 0.976, followed by GBR, 0.958. MLR, RFR and SVR algorithms have the R-squared score of 0.952, 0.934 and 0.865, respectively.