End-to-End-Machine-Learning-Project

Here I look at the exercises for this chapter. Note that these exercise are worked in notebooks presented by the book and thus one can get solutions there if desired. At the time of this writing the book’s solutions can be found https://github.com/ageron/handson-ml2/blob/master/02_end_to_end_machine_learning_project.ipynb

Exercises The following exercises are all based on this chapter’s housing dataset:

1. 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). Don’t worry about what these hyperparameters mean for now. How does the best SVR predictor perform?
2. Try replacing GridSearchCV with RandomizedSearchCV.
3. Try adding a transformer in the preparation pipeline to select only the most important attributes.
4. Try creating a single pipeline that does the full data preparation plus the final prediction.
5. Automatically explore some preparation options using GridSearchCV. if have better answer help me to improve this codes.