please help, there nothing worng in #TODO $5,$ but There is something worng in #TODO $13$

Complete the TODO by getting our data, training the PocketPerceptron class and making predictions for our training and validation data.

Call the data$\_$prep$()$ function to get the setosa$-$vs$-$all data where the sepal length and width features are dropped. To do so$,$ pass the

arguments corresponding to the following descriptions:

Pass the Iris dataset.

Set setosa as the positive class.

Set versicolor and virginica to represent the negative class.

Set the column 'class' to act as the labels$/$targets$.$

Drop the 'sepal length' and 'sepal width' features.

Return all data as NumPy arrays.

Create an instance of the PocketPerceptron. Make sure to pass the arguments corresponding to the following descriptions:

Allow for training to occur for $10$ passes over the data.

$4$ TODO $13\mathrm{.}1$

data $=$ data$\_$prep$($load$\_$iris$(),$ 'setosa', $[\text{'}$versicolor$\text{'},$ 'virginica'$],$ 'class', $[\text{'}$sepal length', 'sepal width'$])$

I data $=$ data$\_$prep$($iris $,$

pos$\_$class $=[],$

neg$\_$class $=[1,2],$

labe$1\_$nane$=$"class",

return$\_$array$=$True$)$

a data $=$ data$\_$prep$($

df$=$iris,

pos$\_$class$=$ "setosa',

neg$\_$class$=$ "versicolor",

label$\_$name$=$ 'labels',

return$\_$array$=$True

a$)$

x$\_$trn$,$ y$\_$trn$,$ X$\_$vld$,$ y$\_$vld$,.,..,$ feature$\_$names $=$ data

II TODO $13\mathrm{.}2$

perceptron $=$ PocketPerceptron$($num$\_$passes$=10)$

perceptron.fit$($X$\_$trn$,$ y$\_$trn$)$

y$\_$hat$\_$trn $=$ perceptron.predict $(x_{{?}_{t}rn})$

todo$\_$check$([$

$($np$.$a$11($np$.$isclose$($X$\_$trn$[4].$flatten$(),[1.,0\mathrm{.}97932518,1\mathrm{.}17336698])),"\text{'}$X$\_$trn$\text{'}$ values are incorrect"$),$

$])$

$($np$.$a$11($np$.$isclose$($perceptron$.$w$\_$best, np$.$array$([-0\mathrm{.}1254,-0\mathrm{.}12089,-0\mathrm{.}2218),$ rtol$=\mathrm{.}1)),$ "best weight values potential$1$y incorrect"$)$

KeyError

Traceback $($most recent call last$)$

Siexthon$-$ineut$-88-$c$8+2$a$75$b$8$a$48>$ in #$,\frac{\text{i}\text{p}\text{e}\text{y}\text{t}\text{h}\text{o}\text{n}-\text{i}\text{n}\text{p}\text{u}\text{t}-79-e103df45c5eg}{21}>2>()$

$1II$ TOOO $13\mathrm{.}1$

$2$ data $=dat{a}_p$rep$(1oa{d}_i$ris$(),[1,2],$ "class", $[\text{'}sepal$ length", "sepa$1$ width"$])$

a data $=dat{a}_p$rep$(iris,po{s}_c$lass $=$ 'setosa',

#$,ne{g}_c$lass$=["versicolor\text{'},$ 'virginica'$],$

$illabe{l}_n$ame$=$"class", $retur{n}_a$rray$=$True$)$

$A1$ frames

$\frac{\text{i}\text{p}\text{e}\text{y}\text{t}\text{h}\text{o}\text{n}-\text{i}\text{n}\text{p}\text{u}\text{t}-79-e103df45c5eg}{21}>inbinariz{e}_c$lasses$(iri{s}_{d}f,po{s}_c$lass, $ne{g}_c$lass$)$

# $T0005\mathrm{.}1$

labels $=iri{s}_{d}f[\text{'}class\text{'}]$

$4005\mathrm{.}2$

KeyError: 'class'TODO$13$