Please follow the instracions and implement codes

Now we want to test our coefficients and see how well we predict the answer. To do with we will need to use the weight vector we just learned. Use np.dotto calculate:

Y =XY^=X

We will then calculate the residual the error that remains between our true times in Y and the calculated times in Yhat.

resid=YY resid=YY^

We will then use these residuals to come up with a single number that tells us how well we did. For this, we will be using the Root Mean Squared Error (RMSE)

RMSE=1N(yy)^2RMSE=1N(yy^)2

To use, this we will use the elementwise multiplication (a*b not np.dot(a,b)), the square root (np.sqrt), and mean (np.mean) functions

#TODO: Calculate Yhat, the residuals and RMSE for both the training and validation sets

def calculate_yhat(X: np.array, B: np.array) -> np.array: return None

def calculate_residuals(Y: np.array, Yhat: np.array) -> np.array: return None

def calculate_rmse(residuals: np.array) -> float: return 0

Yhat = calculate_yhat(X, B_raw) Yhat_validation = calculate_yhat(X_validation, B_raw)

residuals = calculate_residuals(Y, Yhat) residuals_validation = calculate_residuals(Y_validation, Yhat_validation)

rmse = calculate_rmse(residuals) rmse_validation = calculate_rmse(residuals_validation)

print('RMSE:',rmse) print('RMSE Validation:',rmse_validation)