3. Using the linear regression function in NumPy (see page 66 of the ML book) to solve the following problem. You should submit your code in a notebook file through canvas, which should include the implementation and the input data as well as a short discussion about your result. The table below lists the top ten colleges based on mid-career salary and the associated yearly tuition costs. Use the data to check if the higher cost of tuition translate into higher-paying jobs. School Mid-Career Salary (in thousands) Yearly Tuition Princeton 137 28,540 Harvey Mudd 135 40,133 CalTech 127 39,900 US Naval Academy 122 0 West Point 120 0 MIT 118 42,050 Lehigh University 118 43,220 NYU-Poly 117 39,565 Babson College 117 40,400 Stanford 114 54,506 = def linreg(inputs,targets): inputs np.concatenate((inputs,-np.ones((np.shape(inputs)[0],1))),2 axis=1) beta = np.dot(np.dot(np.linalg.inv(np.dot(np.transpose(inputs),2 inputs)),np.transpose(inputs)), targets) outputs np.dot (inputs,beta) 3. Using the linear regression function in NumPy (see page 66 of the ML book) to solve the following problem. You should submit your code in a notebook file through canvas, which should include the implementation and the input data as well as a short discussion about your result. The table below lists the top ten colleges based on mid-career salary and the associated yearly tuition costs. Use the data to check if the higher cost of tuition translate into higher-paying jobs. School Mid-Career Salary (in thousands) Yearly Tuition Princeton 137 28,540 Harvey Mudd 135 40,133 CalTech 127 39,900 US Naval Academy 122 0 West Point 120 0 MIT 118 42,050 Lehigh University 118 43,220 NYU-Poly 117 39,565 Babson College 117 40,400 Stanford 114 54,506 = def linreg(inputs,targets): inputs np.concatenate((inputs,-np.ones((np.shape(inputs)[0],1))),2 axis=1) beta = np.dot(np.dot(np.linalg.inv(np.dot(np.transpose(inputs),2 inputs)),np.transpose(inputs)), targets) outputs np.dot (inputs,beta)