Linear Regression with scikit-learn

Train a Linear Regression model to predict the quantitative measure of the diabetes progression in patients based on their age, sex, body mass index and several blood serum measurements.

1. Load the diabetes dataset from sklearn that contains observations from 442 diabetes patients. You can do this by running the following lines of code:

from sklearn.datasets import load_diabetes

diabetes = load_diabetes()

X = diabetes.data
y = diabetes.target

2. Leave the last 50 observations from the dataset for a test set.

3. Use the rest to build a Linear Regression model.

4. Evaluate your model on the test data.

What is the MSE score of the resulting model on the test data? Use math.ceil() to round up to the nearest integer.