Suppose m=4 students have taken some class, and the class had a midterm exam and a final exam. You have collected a dataset of their scores on the two exams, which is as follows: midterm exam (midterm exam)^2 2 final exam 89 7921 96 72 5184 74 94 8836 87 69 4761 78 You'd like to use polynomial regression to predict a student's final exam score from their midterm exam score. Concretely, suppose you want to fit a model of the form h_\theta(x) = \theta_0 + \theta_1 x_1 + \theta_2 x_2h θ (x)=θ 0 +θ 1 x 1 +θ 2 x 2 , where x_1x 1 is the midterm score and x_2x 2 is (midterm score)^2 2 . Further, you plan to use both feature scaling (dividing by the "max-min", or range, of a feature) and mean normalization. What is the normalized feature x_2^{(2)}x 2 (2) ? (Hint: midterm = 72, final = 74 is training example 2.) Please round off your answer to two decimal places and enter in the text box below.
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Explanation:sorry bro i am not getting so big ques
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ATLEAST SHOW THE TABLE RIGHT MAN
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