Machine-Learning---Google-Developers/ML_recipes_2_iris.py at master · akanshajainn/Machine-Learning---Google-Developers · GitHub

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from sklearn.datasets import load_iris
import numpy as np
from sklearn import tree
#Know Dataset IRIS
iris=load_iris()
print(iris.feature_names)
print(iris.target_names)
print(iris.data[0])
print(iris.target[0])
for i in range(len(iris.target)):
    print('Example %d: label %s, features %s'%(i,iris.target[i],iris.data[i]))

#Splitting in Train Test
test_idx=[0,50,100]
#training data
train_target=np.delete(iris.target,test_idx)
train_data=np.delete(iris.data,test_idx,axis=0)
#testing data
test_target=iris.target[test_idx]
test_data=iris.data[test_idx]
#Training
clf=tree.DecisionTreeClassifier()
clf.fit(train_data,train_target)
#Predicting
print(test_target)
print(clf.predict(test_data))
#Visualisation
from sklearn.externals.six import StringIO
import pydotplus
dot_data=StringIO()
tree.export_graphviz(clf,
                         out_file=dot_data,
                         feature_names=iris.feature_names,
                         class_names=iris.target_names,
                         filled=True, rounded=True,
                         impurity=False)
graph=pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("iris.pdf")