Q_10.py

def Q_10(self, X_train_scaled, X_test_scaled, y_train, y_test):
    # Task 10: Given the (X_train, y_train) pairs denoting input matrix and output vector respectively,
    # Fit a linear regression model using the close-form solution you learned in class to obtain
    # the coefficients, beta's, as a numpy array of m+1 values (Please recall class lecture).
    # Then using the computed beta values, predict the test samples provided in the "X_test_scaled"
    # argument, and let's call your prediction "y_pred".
    # Compute Root Mean Squared Error (RMSE) of your prediction.
    # Finally, return the beta vector, y_pred, RMSE as a tuple.
    # PLEASE DO NOT USE ANY LIBRARY FUNCTION THAT DOES THE LINEAR REGRESSION.

    beta = []
    y_pred = []
    RMSE = -1

    ## YOUR CODE HERE ###



    return (beta, y_pred, RMSE)