Recipe: How to do KMeans Clustering in Python?

How to do KMeans Clustering in Python?

This recipe helps you do KMeans Clustering in Python
In [2]:
## How to do KMeans Clustering in Python
def Snippet_157():
    print(format('How to do KMeans Clustering in Python','*^82'))

    import warnings

    # load libraries
    from sklearn import datasets
    from sklearn.preprocessing import StandardScaler
    from sklearn.cluster import KMeans
    import pandas as pd
    import seaborn as sns
    import matplotlib.pyplot as plt

    # Load data
    iris = datasets.load_iris()
    X =; data = pd.DataFrame(X)
    cor = data.corr()
    # Plot correlation using seaborn
    fig = plt.figure(figsize=(12,10));
    sns.heatmap(cor, square = True);

    # Standarize features
    scaler = StandardScaler()
    X_std = scaler.fit_transform(X)

    # Conduct KMeans Clustering
    clt = KMeans(n_clusters=3)

    # Train model
    model =

    # Predict clusters
    clusters = pd.DataFrame(model.fit_predict(X_std))
    data['Cluster'] = clusters

    # Visualise cluster membership
    fig = plt.figure(figsize=(12,10)); ax = fig.add_subplot(111)
    scatter = ax.scatter(data[0],data[1], c=data['Cluster'],s=50)
    ax.set_title('KMeans Clustering')
    ax.set_xlabel('X0'); ax.set_ylabel('X1')

**********************How to do KMeans Clustering in Python***********************

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