Machine Learning, Data Science, Support Vector Regression.
here we select RBF(a #gaussian type) kernel. regressor = SVR(kernel='rbf') regressor.fit(X,y) #5 Predicting a new result y_pred = regressor.predict(6.5) The prediction output is 130002. This prediction is not good enough. #6 Visualising the Support Vector Regression results plt.scatter(X, y, color