Hello I need help with following python problem.. Thank you in advance..! 1g) Defining the train & test setsNow, we'll instead use sklearn 's train_test_split() function here to define our trainand test set. Store train data (predictors) into MR_train_X and labels (outcomes) intoMR_train_Y. Similarly, store test data into MR_test_X and test labels into MR_test_Y .In addition to providing the predictors ( MR_X ) and outcomes ( MR_Y ) to the function, we willuse the following arguments for this task:test_size : 0.2random_state : 2001 # YOUR CODE HEREMR_train_Y.shape [0]MR_test_Y.shape [0]1 assert MR_train_X.shape [0]2 assert MR_test_X. shape [0]34 assert len (MR_train_X)5 assert len(MR_test_Y)40001000

SUMMARY: - We get the output after applying all logic we get the output. Answer: - First thing that we will understand here is what actually Train / Test is. Train or Test is a method to measure your model for accuracy. It is called Train/Test because you split the the data set into two sets: a training set and a testing set.’ And in this 80% is for training. And for testing 20% is training. CODE: - import numpy import matplotlib.pyplot as plt numpy.random.seed(2) x = numpy.random.normal(3, 1, 100) y = numpy.random.normal(150, 40, 100) / x train_x = x[:80] train_y = y[:80] test_x = x[80:] test_y = y[80:] mymodel = numpy.poly1d(numpy.polyfit(train_x, train_y, 4)) myline = numpy.linspace(0, 6, 100) plt.scatter(train_x, train_y) plt.plot(myline, mymodel(myline)) plt.show() CODE: -