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EM algorithm to cluster a Heart Disease Data Set. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering. You can add Java/Python ML library classes/API in the program.
	
    Program:
    from sklearn.cluster import KMeans
    from sklearn import preprocessing
    from sklearn.mixture import GaussianMixture
    from sklearn.datasets import load_iris
    import sklearn.metrics as sm
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    dataset=load_iris()
    X=pd.DataFrame(dataset.data)
    X.columns=['Sepal_Length','Sepal_Width','Petal_Length','Petal_Width']
    y=pd.DataFrame(dataset.target)
    y.columns=['Targets']
    plt.figure(figsize=(14,7))
    colormap=np.array(['red','lime','black'])
    # REAL PLOT
    plt.subplot(1,3,1)
    plt.scatter(X.Petal_Length,X.Petal_Width,c=colormap[y.Targets],s=40)
    plt.title('Real')
    # K-PLOT
    plt.subplot(1,3,2)
    model=KMeans(n_clusters=3)
    model.fit(X)
    predY=np.choose(model.labels_,[0,1,2]).astype(np.int64)
    plt.scatter(X.Petal_Length,X.Petal_Width,c=colormap[predY],s=40)
    plt.title('KMeans')
    # GMM PLOT
    scaler=preprocessing.StandardScaler()
    scaler.fit(X)
    xsa=scaler.transform(X)
    xs=pd.DataFrame(xsa,columns=X.columns)
    gmm=GaussianMixture(n_components=3)
    gmm.fit(xs)
    y_cluster_gmm=gmm.predict(xs)
    plt.subplot(1,3,3)
    plt.scatter(X.Petal_Length,X.Petal_Width,c=colormap[y_cluster_gmm],s=40)
    plt.title('GMM Classification')

    Output:
    Text(0.5, 1.0, 'GMM Classification')