<jupyter_start><jupyter_text>ObservationFrom above histogram we can see similarities in histogram of Train and test like:    a. Most often both in the case of train and test the steering angle are at 0 deg.    b. Most of the values of steering angle lie between -2 and 2.<jupyter_code>#Model 0: Base line Model: y_test_pred = mean(y_train_i) i.e. Simple mean model
train_mean_y = np.mean(train_y) #Here we are taking mean of all yi's in Training dataset and now we will assume that we have a model-
#- which predicts mean(y_train_i) for each yi in test data (i.e. y_test_pred)

print('Test_MSE(MEAN):%f' % np.mean(np.square(test_y-train_mean_y)) ) #Here we ar calculating our MSE for test dataset whe we have Simple mean model

#Model 1: Lets say ow we have a model which predicts 0 for every y_i in Test dataset (we are assuming this because most of the values of our steering angles are 0 radians)
print('Test_MSE(ZERO):%f' % np.mean(np.square(test_y-0.0)) ) #Calculating MSE for our model o Tests dataset
#We see that for our both aseline models the MSE error we got is 0.19 and now we want to build a model which can give us MSE significantly lower than 0.19<jupyter_output>Test_MSE(MEAN):0.191127
Test_MSE(ZERO):0.190891
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