Comparative Analysis of Support Vector Regression and Linear Regression Models to Predict Apple Inc. Share Prices

Stock price prediction is a complex and important challenge for stock
market participants. The difficulty of predicting stock prices is a major
problem that requires an approach method in obtaining stock price
predictions. This research proposes using machine learning with the
Support Vector Regression (SVR) model and linear regression for
stock price prediction�the dataset used in the daily Apple Inc
historical data from 2018 to 2023. The hyperparameter tuning
technique uses the Grid Search method with a value of k = 5, which
will be tested on the SVR and Linear Regression methods to get the
best prediction model based on the number of cost, epsilon, kernel, and
intercept fit parameters. The test results show that the linear regression
model with all hyperparameters k = 5 with the average taken performs
best with a True intercept fit value. The resulting model can get an
excellent error value, namely the RMSE value of 0.931231 and MSE
of 0.879372. This finding confirms that the linear regression model in
this configuration is a good choice for predicting stock prices.