Solve using Python:

Find the weight vector w when the Boston house price problem is solved with the linear prediction model Xw = by the least-squares method. Matrix and vector data can be obtained as follows. (X This is the question related to question 5.) from sklearn.datasets import load_boston boston=load_boston() X=boston.data y=boston.target The meaning of each column of matrix X is as follows. CRIM: crime rate INDUS: Non-retail commercial area ratio NOX: Nitric Oxide Concentration RM: Number of rooms per house LSTAT: Proportion of the lower class of the population B: Proportion of black people in the population PTRATIO: Student/Teacher Ratio ZN: Percentage of residential areas exceeding 25,000 square feet CHAS: 1 if located on the Charles River border, 0 otherwise AGE: Percentage of houses built before 1940 RAD: Distance to radial highway DIS: Weighted average distance to 5 Boston Job Centers TAX: property tax rate 1) Run the program above to check whether the magnitude or sign of the weight vector obtained from running the program is consistent the common notion. In order to find it, interpret the printed output for all the factors suggested above. (X Write the interpreted output like "the house price is in inverse proportion to the crime rate (CRIM).) 2) Explain how the result differs from the value obtained in Question 5