EM and kmeans

import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.cluster import KMeans
import pandas as pd
import numpy as np
iris=datasets.load_iris()
X=pd.DataFrame(iris.data)
X.columns=['Sepal_Length','Sepal_Width','Petal_Length','Petal_Width']
y=pd.DataFrame(iris.target)
y.columns=['Targets']
model=KMeans(n_clusters=3)
model.fit(X)
plt.figure(figsize=(14,14))
colormap=np.array(['red','lime','black'])
plt.subplot(2,2,1)
plt.scatter(X.Petal_Length,X.Petal_Width,c=colormap[y.Targets],s=40)
plt.title('Real Clusters')
plt.xlabel('Petal Length')
plt.ylabel('Petal width')
plt.subplot(2,2,2)
plt.scatter(X.Petal_Length,X.Petal_Width,c=colormap[model.labels_],s=40)
plt.title('K-Means Clustering')
plt.xlabel('Petal Length')
plt.ylabel('Petal Width')
plt.subplot(2,2,2)
plt.scatter(X.Petal_Length,X.Petal_Width,c=colormap[model.labels_],s=40)
plt.title('K-Means Clustering')
plt.ylabel('Petal Width')
from sklearn import preprocessing
scaler=preprocessing.StandardScaler()
scaler.fit(X)
xsa=scaler.transform(X)
xs=pd.DataFrame(xsa,columns=X.columns)
from sklearn.mixture import GaussianMixture
gmm=GaussianMixture(n_components=3)
gmm.fit(xs)
gmm_y=gmm.predict(xs)
plt.subplot(2,2,3)
plt.scatter(X.Petal_Length,X.Petal_Width,c=colormap[gmm_y],s=40)
plt.title('GMM Clustering')
plt.xlabel('Petal Length')
plt.ylabel('Petal width')
print('Observation:The GMM using EM algo based clustering matched the true labels more closely than KMeans.')