We will continue the analysis started in Problem $4$ by calculating the r$-$squared score for our predictions. The

first step in this process in to calculate the mean of the observed values.

Complete the following steps in a single code cell:

$1.$ Use the map$()$ transformation along with a lambda function to select the first element of each tuple

in the pairs RDD$.$ Call the mean$()$ method of the resulting RDD$,$ storing the result in a variable

named mean.

$2.$ Print mean.

Note that this calculation might take a couple of minutes to complete.

We will now calculate the sum of the squared deviations between each observed value and their mean. This

quantity is sometimes referred to as SST$,$ or $$total sum of squared deviations$.$

Complete the following steps in a single code cell:

$1.$ Use the map$()$ transformation along with a lambda function to calculate the square of the difference

between each observed value in pairs and mean. Call the sum$()$ method of the resulting RDD$,$

storing the result in a variable named SST$.$

$2.$ Print SST$.$

We will now calculate the r$-$squared score for the predictions. The formula for this value is given as follow: $2=$

$1$

$$

Complete the following steps in a single code cell:

$1.$ Use SSE and SST to calculate r$-$squared, storing the result in a variable named r$2.$

$2.$ Print r$2.$