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Cluster Analysis in Python
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Course Content

Cluster Analysis in Python

Cluster Analysis in Python

1. K-Means Algorithm
Exploratory Data AnalysisWhat is a K-Means Algorithm?Defining the Number of ClustersCase 1: Three Distinct ClustersCase 2: Four Distinct ClustersClustering Weather DataFebruary vs July Average TemperaturesVisualizing the Dynamics Across Clusters
2. K-Medoids Algorithm
What is a K-Medoids Algorithm?Cluster CentersSilhouette ScoresK-Medoids and the Weather DataMean Yearly Temperatures Across ClustersComparing the Dynamics
3. Hierarchical Clustering
What is a Hierarchical Clustering?DendrogramsSetting Parameters: LinkageRand IndexThe Weather Data and LinkagesDeciding the Number of ClustersWeather Data: Complete and Ward LinkagesHow Similar are the Results?
4. Spectral Clustering
What is a Spectral Clustering?Peculiarity of Spectral ClusteringSetting Parameters: AffinityDefining the Number of ClustersIs 4 the Optimal Number of Clusters?Comparing the Trends Across Clusters

bookMean Yearly Temperatures Across Clusters

The last chart we got was even harder to interpret than two chapters ago. But if we are talking about 'peeks', the number 4 best fits it.

Let's compare the yearly average temperatures across 4 predicted clusters.

Task

Calculate the yearly average temperatures across each cluster. The structure of data is shown below. [object Object]

Follow the next steps:

  1. Create a KMedoids model with 4 clusters named model.
  2. Fit the 3-15 (these are positions, not indices) columns of data to model.
  3. Add the 'prediction' column to data with predicted by model labels.
  4. Group the data DataFrame by the prediction column, then apply the .mean() function twice: the first call will calculate the monthly means, the second one (with axis = 1) will calculate the yearly averages.

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Section 2. Chapter 5
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bookMean Yearly Temperatures Across Clusters

The last chart we got was even harder to interpret than two chapters ago. But if we are talking about 'peeks', the number 4 best fits it.

Let's compare the yearly average temperatures across 4 predicted clusters.

Task

Calculate the yearly average temperatures across each cluster. The structure of data is shown below. [object Object]

Follow the next steps:

  1. Create a KMedoids model with 4 clusters named model.
  2. Fit the 3-15 (these are positions, not indices) columns of data to model.
  3. Add the 'prediction' column to data with predicted by model labels.
  4. Group the data DataFrame by the prediction column, then apply the .mean() function twice: the first call will calculate the monthly means, the second one (with axis = 1) will calculate the yearly averages.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 5
toggle bottom row

bookMean Yearly Temperatures Across Clusters

The last chart we got was even harder to interpret than two chapters ago. But if we are talking about 'peeks', the number 4 best fits it.

Let's compare the yearly average temperatures across 4 predicted clusters.

Task

Calculate the yearly average temperatures across each cluster. The structure of data is shown below. [object Object]

Follow the next steps:

  1. Create a KMedoids model with 4 clusters named model.
  2. Fit the 3-15 (these are positions, not indices) columns of data to model.
  3. Add the 'prediction' column to data with predicted by model labels.
  4. Group the data DataFrame by the prediction column, then apply the .mean() function twice: the first call will calculate the monthly means, the second one (with axis = 1) will calculate the yearly averages.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

The last chart we got was even harder to interpret than two chapters ago. But if we are talking about 'peeks', the number 4 best fits it.

Let's compare the yearly average temperatures across 4 predicted clusters.

Task

Calculate the yearly average temperatures across each cluster. The structure of data is shown below. [object Object]

Follow the next steps:

  1. Create a KMedoids model with 4 clusters named model.
  2. Fit the 3-15 (these are positions, not indices) columns of data to model.
  3. Add the 'prediction' column to data with predicted by model labels.
  4. Group the data DataFrame by the prediction column, then apply the .mean() function twice: the first call will calculate the monthly means, the second one (with axis = 1) will calculate the yearly averages.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 5
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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