IrisCaseStudy(DecisionTreeClassifier)2.py

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

def MarvellousDecisionTreeClassifier():
    Dataset = load_iris()       # 1 Load the data

    Data = Dataset.data
    Target = Dataset.target

    # step 2. manipulating the data
    Data_train, Data_test, Target_train, Target_test = train_test_split(Data, Target, test_size = 0.5)

    Classifier = DecisionTreeClassifier()

    # step3. Build the model
    Classifier.fit(Data_train, Target_train)

    # step 4 .test the model
    Predictions = Classifier.predict(Data_test)

    Accuracy = accuracy_score(Target_test, Predictions)

    # step 5 . Improve - missing

    return Accuracy

def main():
    Ret = MarvellousDecisionTreeClassifier()

    print("Acuracy of Iris dataset with Decision Tree Classifier is ",Ret * 100)

if __name__ == "__main__":
    main()