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For Project 1, fit the California Housing Data using a Support Vector Regression (SVR).

Use the code that we have been using in the previous notebooks but use

from sklearn.svm import SVR

svm_reg = SVR()

instead of

forest_reg = RandomForestRegressor(random_state=42)

Use a grid search to fine tune the model. Use the following parameter grid:

param_grid = [
{‘kernel’: [‘linear’], ‘C’: [10., 30., 100., 300., 1000., 3000., 10000., 30000.0]},
{‘kernel’: [‘rbf’], ‘C’: [1.0, 3.0, 10., 30., 100., 300., 1000.0],
‘gamma’: [0.01, 0.03, 0.1, 0.3, 1.0, 3.0]},
]

Determine the best values of the hyperparameters and report the RMSE on the test data set.

Show all your code and output in a Jupyter notebook. Also, comment (using markdown) on what you are doing in each step.

In the Jupyter notebook, put Project 1 in a heading at the top. Underneath that, put your first and last name as a subheading (use three #’s for the subheading).

  
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