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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 Dataset
Step2:  Standardize Features
Step3:  Conduct k-Means Clustering
MiniBatchKMeans works similarly to KMeans, with one significance difference
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Python Code:
# Load libraries
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import MiniBatchKMeans
Explanation: Title: Mini-Batch k-Means Clustering
Slug: minibatch_k-means_clustering
Summary: How to conduct mini-batch k-means clustering in scikit-learn.
Date: 2017-09-22 12:00
Category: Machine Learning
Tags: Clustering
Authors: Chris Albon  
Mini-batch k-means works similarly to the k-means algorithm discussed in the last recipe. Without going into too much detail, the difference is that in mini-batch k-means the most computationally costly step is conducted on only a random sample of observations as opposed to all observations. This approach can significantly reduce the time required for the algorithm to find convergence (i.e. fit the data) with only a small cost in quality.
Preliminaries
End of explanation
# Load data
iris = datasets.load_iris()
X = iris.data
Explanation: Load Iris Flower Dataset
End of explanation
# Standarize features
scaler = StandardScaler()
X_std = scaler.fit_transform(X)
Explanation: Standardize Features
End of explanation
# Create k-mean object
clustering = MiniBatchKMeans(n_clusters=3, random_state=0, batch_size=100)
# Train model
model = clustering.fit(X_std)
Explanation: Conduct k-Means Clustering
MiniBatchKMeans works similarly to KMeans, with one significance difference: the batch_size parameter. batch_size controls the number of randomly selected observations in each batch. The larger the the size of the batch, the more computationally costly the training process.
End of explanation
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