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Learn Case 2: Four Distinct Clusters | K-Means Algorithm
Cluster Analysis in Python

bookCase 2: Four Distinct Clusters

Well, I think that the model did good work. But what if we try to divide the points into 4 groups?

Task

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  1. Import necessary libraries with their standard aliases. These are pandas (pd), matplotlib.pyplot (plt), seaborn (sns), and KMeans from sklearn.cluster.
  2. Create a KMeans model object with 4 clusters.
  3. Fit the data to the model.
  4. Predict the labels for data. Save the result within the 'prediction' column of data.
  5. Build scatter plot for 'x' and 'y' columns of data with each point being painted with respect to the 'prediction' column.

Solution

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SectionΒ 1. ChapterΒ 5
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bookCase 2: Four Distinct Clusters

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Well, I think that the model did good work. But what if we try to divide the points into 4 groups?

Task

Swipe to start coding

  1. Import necessary libraries with their standard aliases. These are pandas (pd), matplotlib.pyplot (plt), seaborn (sns), and KMeans from sklearn.cluster.
  2. Create a KMeans model object with 4 clusters.
  3. Fit the data to the model.
  4. Predict the labels for data. Save the result within the 'prediction' column of data.
  5. Build scatter plot for 'x' and 'y' columns of data with each point being painted with respect to the 'prediction' column.

Solution

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Everything was clear?

How can we improve it?

Thanks for your feedback!

close

Awesome!

Completion rate improved to 3.57
SectionΒ 1. ChapterΒ 5
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