Try a Support Vector Machine regressor $($sklearn$.$svm$.$SVR$)$ with various hyperparameters, such as kernel$=$"linear" $($with various values for the C hyperparameter$)$ or kernel$="$rbf$"($with various values for the C and gamma hyperparameters$).$ Note that SVMs don't scale well to large datasets, so you should probably train your model on just the first $5,000$ instances of the training set and use only $3-$fold cross$-$validation, or else it will take hours. Don't worry about what the hyperparameters mean for now $($see the SVM notebook if you're interested$).$ How does the best SVR predictor perform?