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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Example for Support Vector Machine (SVM) using the Iris Data
First we need the standard import
Step1:  Load the Data
Step2:  Look at the data
it's a good idea to look at the data a little bit, know the shapes, etc...
Step3:  since you can't plot 4 dimensions, try plotting some 2D subsets
I don't like the automatic placement of the legend, so lets set it manually
Step4:  I don't want to do the classification on this subset, so make sure to use the entire data set.
Classification
First, we choose a classifier
Step5:  Split the data into test and train subsets...
Step6:  ...and then train...
Step7:  some classifiers have properties that are useful to look at.  Naive Bayes has  means and stddevs...
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Python Code:
from classy import *
Explanation: Example for Support Vector Machine (SVM) using the Iris Data
First we need the standard import
End of explanation
data=load_excel('data/iris.xls',verbose=True)
Explanation: Load the Data
End of explanation
print(data.vectors.shape)
print(data.targets)
print(data.target_names)
print(data.feature_names)
Explanation: Look at the data
it's a good idea to look at the data a little bit, know the shapes, etc...
End of explanation
subset=extract_features(data,[0,2])
plot2D(subset,legend_location='upper left')
Explanation: since you can't plot 4 dimensions, try plotting some 2D subsets
I don't like the automatic placement of the legend, so lets set it manually
End of explanation
C=SVM()
Explanation: I don't want to do the classification on this subset, so make sure to use the entire data set.
Classification
First, we choose a classifier
End of explanation
data_train,data_test=split(data,test_size=0.2)
Explanation: Split the data into test and train subsets...
End of explanation
timeit(reset=True)
C.fit(data_train.vectors,data_train.targets)
print("Training time: ",timeit())
print("On Training Set:",C.percent_correct(data_train.vectors,data_train.targets))
print("On Test Set:",C.percent_correct(data_test.vectors,data_test.targets))
Explanation: ...and then train...
End of explanation
C.dual_coef_
Explanation: some classifiers have properties that are useful to look at.  Naive Bayes has  means and stddevs...
End of explanation
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