$2.$ Preprocessing tasks:

You are requested to repeat all the experiments in Section $4$ below using the following

normalization techniques:

a$.$ Normalize the feature variables using z$-$scores

b$.$ Normalize the feature variables using min$-$max

$3.$ Data split into train and test sets:

You are requested to repeat all the experiments in Section $4$ below using three splits of

the data. You can do that by fixing the random$\_$state parameter to $1,20$ and $40.$ This will

generate $3$ different train and test sets. Then:

a$.$ Report the R$2$ and MSE for each test split, as described in Section $4.$

b$.$ Report the average R$2$ and MSE for all test splits, as described in Section $4.$

$3$

$4.$ Regression:

You need to use all of the following regression techniques:

a$.$ Linear regression

b$.$ SVM with linear kernel

c$.$ SVM with polynomial kernel

d$.$ SVM with RBF kernel

e$.$ Neural Networks

In summary, you need to carry out the experiments in the following manner:

Loop for both normalization techniques of Section $2$ of the project details

Loop for each of the three data splits of Section $3$ of the project details

Loop for each classifier or regressor of Section $4$ of the project details

So the total number of experiments is:

$2($normalizations$)$ x $3($data splits$)$ x $5($regressors$)=30$ experiments