Load up the wine data set. This can be easily done using the command load "wine.data". Make sure

the wine.data file is in the same directory as your script file. The data set description can be found in

wine.names. Considering only the ff$.$ features: Alcohol Content $(x-$axis$)$ and Proline Content $(y-$axis$),$

plot the data set using the gscatter command. Classify the following data points: $(11,500),$

$(12\mathrm{.}5,800),$ and $(14,1400).$

Train a k$-$NN classifier with $k=7$ using the whole wine dataset. Use only the following features:

alcohol content, proline content.

Store the label of the $3$ points in a $3-$by$-1$ matrix called: id$2.$

The plot of both the dataset and the points should be shown in one plot in Figure $2.$ You should

use "hold on$"$ to show both the dataset and the points on the same plot.

Observe proper plot titles and axis labels.

Repeat #$2$ but perform feature scaling. Use the same features as in #$3.$ but

Make sure to normalize the training dataset before using the fitcknn command. No need to

normalize the class output feature.

Make sure to normalize the input data points before using the predict command. You have to use

the same mean and standard deviation values used in normalizing your training dataset.

Store the label of the $3$ points in a $3-$by$-1$ matrix called: id$3.$

The plot of both the dataset and the points should be shown in one plot in Figure $3.$ You should

use "hold on$"$ to show both the dataset and the points on the same plot.

Observe proper plot titles and axis labels.