Using Python, and its SkLearn Library, implement Linear Regression on California

Housing Dataset.

The dataset can be found in the SkLearn library. At first pick an $x$ variable $($There are

around $9x$ variables$)$ and a $Y$ variable which is the median price of a house in California,

and predict the $Y$ value using simple regression.

Then use all of the $x$ variables to predict the $Y$ value.

You have the following tasks;

Import the California Housing dataset

Split the dataset for the training and the test set, if there are any empty parts in

the dataset you need to remove it using dropna$()$ method from pandas library

Pick the number of the rooms $($named totalrooms in the dataset$)x$ variable and

implement Simple regression to find the median house price Y $($named as median$-$

housevalue in the dataset$)$

Evaluate the performance, and print the $R^2$ and MSE results

Use all of the $x$ values to implement multiple regression to find the median house

price $Y$

Evaluate the performance, and print the $R^2$ and MSE results

$($Optional$)$ You can visualize the regression by using matplotlib.pyplot library of

Python.