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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 Data
Step2:  Create Random Forest Classifier
Step3:  Train Random Forest Classifier
Step4:  Predict Previously Unseen Observation
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Python Code:
# Load libraries
from sklearn.ensemble import RandomForestClassifier
from sklearn import datasets
Explanation: Title: Random Forest Classifier
Slug: random_forest_classifier
Summary: Training a random forest classifier in scikit-learn.
Date: 2017-09-21 12:00
Category: Machine Learning
Tags: Trees And Forests
Authors: Chris Albon  
Preliminaries
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# Load data
iris = datasets.load_iris()
X = iris.data
y = iris.target
Explanation: Load Iris Data
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# Create random forest classifer object that uses entropy
clf = RandomForestClassifier(criterion='entropy', random_state=0, n_jobs=-1)
Explanation: Create Random Forest Classifier
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# Train model
model = clf.fit(X, y)
Explanation: Train Random Forest Classifier
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# Make new observation
observation = [[ 5,  4,  3,  2]]
              
# Predict observation's class    
model.predict(observation)
Explanation: Predict Previously Unseen Observation
End of explanation
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