K-Means clustering in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the KMeans class from scikit-learn.

ML Python

from sklearn.cluster import [1]

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AKMeans

BLinearRegression

CDecisionTreeClassifier

DRandomForest

Attempts:

3 left

2fill in blank

medium

Complete the code to create a KMeans model with 3 clusters.

ML Python

model = KMeans(n_clusters=[1])

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A10

B1

C5

D3

Attempts:

3 left

3fill in blank

hard

Fix the error in the code to fit the KMeans model on data X.

ML Python

model.[1](X)

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Afit

Bpredict

Ctransform

Dscore

Attempts:

3 left

4fill in blank

hard

Fill both blanks to create a dictionary of cluster labels for each data point.

ML Python

labels_dict = {i: model.[1][i] for i in range([2])}

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Alabels_

Blen(X)

Ccluster_centers_

Dinertia_

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to compute and print the silhouette score for the clustering.

ML Python

from sklearn.metrics import [1]
score = [2](X, model.[3])
print('Silhouette Score:', score)

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Asilhouette_score

Blabels_

Cpredict

Dfit_predict

Attempts:

3 left