Do the following steps:

Create testBrnli by $0-1$ encoding testy variable

Transform testX into pandas data frame by with encoded values of all the features. The transformed data frame should still be called testX.

Create vector $($i$.$e$.$ numpy arrray$)$ of predictions, called yhattest, based on the feature values from the test data.

If everything is fine, first couple of values of yhattest should be $1,0,0,1,1,0,0,1,0.$

est

$$

# your code here

from sklearn.preprocessing import LabelEncoder, OrdinalEncoder

enc $=$ OrdinalEncoder$()$

le $=$ LabelEncoder$()$

testBrnli $=$ le$.$fit$\_$transform$($testy$)$

testX $=$ pd$.$DataFrame$($testX$,$ columns$=$colnames$)$

model $=$ CategoricalNB$()$

model.fit$($testX$,$ testBrnli$)$

yhattest $=$ model.predict$($testX$)$

yhattest$[$:$9]$