A Practical Guide to Machine Learning with Python/

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K-Means Clustering Implementation Steps: 4 to 6

K-Means Clustering Implementation Steps: 4 to 6

Continue to become familiar with the implementation steps (4-6) of k-means clustering.

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4) Predict

By using the predict function under a new variable (model_predict), you can execute the model and generate the centroid coordinates using cluster_centers_.

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main.py
advertising.csv
#4) Predict
model_predict = model.predict(X)
centroids = model.cluster_centers_
print(model.cluster_centers_)

5) Visualize the output

It’s now time to plot the clusters on a scatterplot using two sets of elements. The first is the four color-coded clusters produced using the k-means model, which are stored under the variable named model_predict. The second is the cluster centroids, which are stored under the variable named centroids.

The centroids are black with a marker size (s) of 200 and an alpha of 1. Alpha can take any float number between 0 and 1.0, with 0 equal to ...