Now let's train a Random Forest model the same way we trained the Decision Tree model and see how it performs.
Import the RandomForestRegressor class from Scikit-learn
from sklearn.ensemble import <<your code goes here>>
Now let's train the model with our training data
forest_reg = RandomForestRegressor(n_estimators=30, random_state=42)
forest_reg.<<your code goes here>>(housing_prepared, housing_labels)
Now we will predict using out model
housing_predictions = forest_reg.<<your code goes here>>(housing_prepared)
Finally, we will evaluate our model
forest_mse = mean_squared_error(housing_labels, housing_predictions)
forest_rmse = np.sqrt(forest_mse)
forest_rmse
Note - Having trouble with the assessment engine? Follow the steps listed here
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