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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

Case 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

  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.

Task

  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.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 1. Chapter 5
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Case 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

  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.

Task

  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.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 1. Chapter 5
toggle bottom row

Case 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

  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.

Task

  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.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

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

Task

  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.

Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 5
Switch to desktop for real-world practiceContinue from where you are using one of the options below
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