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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:
 Title
Step1:  Load Iris Dataset
Step2:  Create Decision Tree Using Gini Impurity
Step3:  Train Model
Step4:  Create Observation To Predict
Step5:  Predict Observation
Step6:  View Predicted Probabilities
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Python Code:
# Load libraries
from sklearn.tree import DecisionTreeClassifier
from sklearn import datasets
Explanation: Title: Decision Tree Classifier
Slug: decision_tree_classifier
Summary: Training a decision tree classifier in scikit-learn.
Date: 2017-09-19 12:00
Category: Machine Learning
Tags: Trees And Forests
Authors: Chris Albon  
<a alt="Gini Index" href="https://machinelearningflashcards.com">
    <img src="decision_tree_classifier/Gini_Index_print.png" class="flashcard center-block">
</a>
Preliminaries
End of explanation
# Load data
iris = datasets.load_iris()
X = iris.data
y = iris.target
Explanation: Load Iris Dataset
End of explanation
# Create decision tree classifer object using gini
clf = DecisionTreeClassifier(criterion='gini', random_state=0)
Explanation: Create Decision Tree Using Gini Impurity
End of explanation
# Train model
model = clf.fit(X, y)
Explanation: Train Model
End of explanation
# Make new observation
observation = [[ 5,  4,  3,  2]]
Explanation: Create Observation To Predict
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# Predict observation's class    
model.predict(observation)
Explanation: Predict Observation
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# View predicted class probabilities for the three classes
model.predict_proba(observation)
Explanation: View Predicted Probabilities
End of explanation
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