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Cluster Analysis in Python
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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

February vs July Average Temperatures

Well, as you remember, there are no 100% correct answers to clustering problems. For the last task you solved it seems like 5 clusters might be a good option.

Let's visualize the results of clustering into 5 groups by building the scatter plot for average February vs July temperatures, which are one of the coldest and hottest months respectively.

Tarea

Table
  1. Create a KMeans model named model with 5 clusters.
  2. Fit the numerical columns of data (2 - 13 indices) to model.
  3. Add the 'prediction' column to the data DataFrame with predicted by model labels.
  4. Build a scatter plot of average 'Feb' vs 'Jul' temperatures, having each point colored with respect to the 'prediction' column of the data DataFrame.

Tarea

Table
  1. Create a KMeans model named model with 5 clusters.
  2. Fit the numerical columns of data (2 - 13 indices) to model.
  3. Add the 'prediction' column to the data DataFrame with predicted by model labels.
  4. Build a scatter plot of average 'Feb' vs 'Jul' temperatures, having each point colored with respect to the 'prediction' column of the data DataFrame.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 1. Capítulo 7
toggle bottom row

February vs July Average Temperatures

Well, as you remember, there are no 100% correct answers to clustering problems. For the last task you solved it seems like 5 clusters might be a good option.

Let's visualize the results of clustering into 5 groups by building the scatter plot for average February vs July temperatures, which are one of the coldest and hottest months respectively.

Tarea

Table
  1. Create a KMeans model named model with 5 clusters.
  2. Fit the numerical columns of data (2 - 13 indices) to model.
  3. Add the 'prediction' column to the data DataFrame with predicted by model labels.
  4. Build a scatter plot of average 'Feb' vs 'Jul' temperatures, having each point colored with respect to the 'prediction' column of the data DataFrame.

Tarea

Table
  1. Create a KMeans model named model with 5 clusters.
  2. Fit the numerical columns of data (2 - 13 indices) to model.
  3. Add the 'prediction' column to the data DataFrame with predicted by model labels.
  4. Build a scatter plot of average 'Feb' vs 'Jul' temperatures, having each point colored with respect to the 'prediction' column of the data DataFrame.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 1. Capítulo 7
toggle bottom row

February vs July Average Temperatures

Well, as you remember, there are no 100% correct answers to clustering problems. For the last task you solved it seems like 5 clusters might be a good option.

Let's visualize the results of clustering into 5 groups by building the scatter plot for average February vs July temperatures, which are one of the coldest and hottest months respectively.

Tarea

Table
  1. Create a KMeans model named model with 5 clusters.
  2. Fit the numerical columns of data (2 - 13 indices) to model.
  3. Add the 'prediction' column to the data DataFrame with predicted by model labels.
  4. Build a scatter plot of average 'Feb' vs 'Jul' temperatures, having each point colored with respect to the 'prediction' column of the data DataFrame.

Tarea

Table
  1. Create a KMeans model named model with 5 clusters.
  2. Fit the numerical columns of data (2 - 13 indices) to model.
  3. Add the 'prediction' column to the data DataFrame with predicted by model labels.
  4. Build a scatter plot of average 'Feb' vs 'Jul' temperatures, having each point colored with respect to the 'prediction' column of the data DataFrame.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Well, as you remember, there are no 100% correct answers to clustering problems. For the last task you solved it seems like 5 clusters might be a good option.

Let's visualize the results of clustering into 5 groups by building the scatter plot for average February vs July temperatures, which are one of the coldest and hottest months respectively.

Tarea

Table
  1. Create a KMeans model named model with 5 clusters.
  2. Fit the numerical columns of data (2 - 13 indices) to model.
  3. Add the 'prediction' column to the data DataFrame with predicted by model labels.
  4. Build a scatter plot of average 'Feb' vs 'Jul' temperatures, having each point colored with respect to the 'prediction' column of the data DataFrame.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 1. Capítulo 7
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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