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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 Flower Data
Step2:  Standardize Features
Step3:  Conduct Agglomerative Clustering
In scikit-learn, AgglomerativeClustering uses the linkage parameter to determine the merging strategy to minimize the 1) variance of merged clusters (ward), 2) average of distance between observations from pairs of clusters (average), or 3) maximum distance between observations from pairs of clusters (complete). 
Two other parameters are useful to know. First, the affinity parameter determines the distance metric used for linkage (minkowski, euclidean, etc.). Second, n_clusters sets the number of clusters the clustering algorithm will attempt to find. That is, clusters are successively merged until there are only n_clusters remaining.
Step4:  Show Cluster Membership
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Python Code:
# Load libraries
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import AgglomerativeClustering
Explanation: Title: Agglomerative Clustering
Slug: agglomerative_clustering
Summary: How to conduct agglomerative clustering in scikit-learn.
Date: 2017-09-22 12:00
Category: Machine Learning
Tags: Clustering
Authors: Chris Albon  
<a alt="Agglomerative Clustering" href="https://machinelearningflashcards.com">
    <img src="agglomerative_clustering/Aggomerative_Clustering_print.png" class="flashcard center-block">
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Preliminaries
End of explanation
# Load data
iris = datasets.load_iris()
X = iris.data
Explanation: Load Iris Flower Data
End of explanation
# Standarize features
scaler = StandardScaler()
X_std = scaler.fit_transform(X)
Explanation: Standardize Features
End of explanation
# Create meanshift object
clt = AgglomerativeClustering(linkage='complete', 
                              affinity='euclidean', 
                              n_clusters=3)
# Train model
model = clt.fit(X_std)
Explanation: Conduct Agglomerative Clustering
In scikit-learn, AgglomerativeClustering uses the linkage parameter to determine the merging strategy to minimize the 1) variance of merged clusters (ward), 2) average of distance between observations from pairs of clusters (average), or 3) maximum distance between observations from pairs of clusters (complete). 
Two other parameters are useful to know. First, the affinity parameter determines the distance metric used for linkage (minkowski, euclidean, etc.). Second, n_clusters sets the number of clusters the clustering algorithm will attempt to find. That is, clusters are successively merged until there are only n_clusters remaining.
End of explanation
# Show cluster membership
model.labels_
Explanation: Show Cluster Membership
End of explanation
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