$1.$A $-$ Load the Dataset

ints $=$ at rend$\_$gevi'frent,onericte'?

pritibts$-$inwet

Atselasel$]$

$(3$$$3\%,2\%$

$1.$B $-$ Create Feature and Label Arrays

This dataset contains $10$ numerical features, two categorical features, and a single label. The frst $10$ columns of the dataset represent the

numerical features. The next two columns, named uslderness$\_$area and so$11\_$type, represent the categorical features. The final column,

cover$\_$Type, provides the integer$-$encoded labels. We will ignore the categorical features for this assignment.

In the cell below, create a feature amay and a label array. The feature amay should contains only the $10$ numerical features.

The labels are encoded using integers $1-7,$ but they need to be encoded using integers $0-6.$ When creating the label array, please subtract $1$

from all of the label values.

After creating the two arrays, print the shape of each array.

$[]$ X $=$ data$\mathrm{.}1$loc$[$i$,110],$values

y a $($data$[\text{'}$Cover$\_$Type'$]-1)$

print$($x$,$ shase$)$

print$($y$.$shape$)$

$\backslash (15120,10)$

$(25120,)$

$1.$C $-$ Build and Train the Model

Use in$.$sequential to construct a neural network for use with this problem. You can use linear and Relu layers for this part You can select

any architecture you would lice, as long as you meet the goal described below. Wrap your network using the nuet class provided in the course

module.

After creating the model, call the train$\_$node$1()$ method to train the model on the forest cover data. Set val$\_$selitae. $2$ and seede$1.$ Select

values the epochs, batch$\_$size, Ir$,$ and updates, parameters.

To Imit the number of lines of ouput displayed to fewer than $100,$ please select a value for updates that is not less than epochs $/100.$

Your goal is to reach a final validation accuracy of at least $0\mathrm{.}7.$ To show that your model is relatively stable, your validation accuracy should be

above this threshold for the last several training epochs. Adjust the training parameters untll this goal is met.

$1.$D $-$ Display the Training Curves

Call the training$\_$curves$()$ method to display the training curves for your model.

Need help with C and D$.$ thank you