Machine learning problem

 Question is attached below:

Regression Load the Boston housing price dataset. The description of the dataset is here: http://facweb.cs.depaul.edu/mobasher/classes/CSC478/Data/housing-dscr.txt Confirm that there are no missing values for each feature (can check for null values). Display a correlation matrix that measures the linear relationship between features (i.e. corr function). From the above, use the features RM and LSTAT for training the model. Explain why these two features are good choices for training. Plot both features separately against the target MEDV. Split the data into 80% training and 20% test, and train with a linear regressor. Use the linear regressor to calculate the RMSE and R2 score. Morgan State University Department of Electrical & Computer Engineering To improve results, create and apply a polynomial regressor (of degree 2)