Problem 6: Linear Regression in Python [5 points] Complete the gaps in the following Python code, which is supposed to calculate regression line for predicting dependent variable Y using predictor variable X, and calculate the RMSE of this prediction on the dataset: data = Table. read_table("data.csv") X = data. column("X") Y = data . column ("Y") cov = np. mean ((X - np. mean(X) ) * (Y - np. mean(Y) ) ) corr = cov / (np. std(X) * np. std(Y) ) slope = * np. std(_) / np. std(. intercept = np. mean(Y) - slope * np. mean(X) predictions = * X + RMSE = np . sqrt (np . mean( (. * * _))