Make the appropriate "design matrix" for multivariate linear regression for this question by inserting a column of ones to the begining of $x$ data

$($we do this to add an intercept to the regression line$).$

Hint: use np$.$ concatenate$(($a$,$ b$),$ axis$=1)$

where $a$ is an array of ones of the same length as the number of features, and $b$ is the input data. Axis $1$ means that we want to add a column

rather than a row.

$[]$ ### Write your code below

$vv$ Part b:

Split the data into training and testing sets. You may use the included scikitlearn train and test function for this step. Please split using an $80\%$

train$/20\%$ test split. Here is an example of how to use the function:

x$\_$train, x$\_$teat, y$\_$train, y$\_$test $=$ train$\_$test$\_$split $,$ test$\_$size $=0\mathrm{.}2)$

Use the train data to solve for the estimated regression weights using the

hat$()=(x^{TT}x{)}^{-1}{x}^{TT}Y$

formula. Be mindful of matrix dimensions!

Use a loop to repeat this whole process five times $($i$.$e$.$ split into train$/$test five times and calculate the weights for each split$)$ and print all

estimated weghts.

$[]$ np$.$ random. seed $($seed$)$

### Write your code below $($don$\text{'}$t forget to label any graphs$).$ I put pass in the loop as a placeholder, please remove it when you write your code

kfolds $=5$

for $i$ in range$($kfolds$)$:

pass