Course Content

Cluster Analysis in Python

1. K-Means Algorithm

Exploratory Data Analysis What is a K-Means Algorithm?Defining the Number of Clusters Case 1: Three Distinct Clusters Case 2: Four Distinct Clusters Clustering Weather Data February vs July Average Temperatures Visualizing the Dynamics Across Clusters

2. K-Medoids Algorithm

What is a K-Medoids Algorithm?Cluster Centers Silhouette Scores K-Medoids and the Weather Data Mean Yearly Temperatures Across Clusters Comparing the Dynamics

3. Hierarchical Clustering

What is a Hierarchical Clustering?Dendrograms Setting Parameters: Linkage Rand Index The Weather Data and Linkages Deciding the Number of Clusters Weather Data: Complete and Ward Linkages How Similar are the Results?

4. Spectral Clustering

What is a Spectral Clustering?Peculiarity of Spectral Clustering Setting Parameters: Affinity Defining the Number of Clusters Is 4 the Optimal Number of Clusters?Comparing the Trends Across Clusters

K-Medoids and the Weather Data

As you can see, there was no such clear peek as in the example. That means that both 3 and 4 clusters may be a good choice!

Let's see what will be the result of using the K-Medoids algorithm for the weather data we used in the previous section. Let's start with defining the optimal number of clusters.

Task

Given cities' average temperatures dataset data. The numerical columns are 3 - 14. [object Object]

Your tasks are:

Using for loop iterate over n_cl. Within the loop:

Create KMedoids model with j clusters named model.
Fit the 2-15 columns of data to the model. Watch out that indices in Python start from 0.
Add silhouette score value to the silhouettes list. Remember to pass two parameters: the data used for fitting (the same 3-15 columns) and predicted by model labels.

Visualize the results using lineplot of sns. Pass n_cl as x parameter and silhouettes as y. Do not forget to apply the .show() method to display the plot.

Task

Given cities' average temperatures dataset data. The numerical columns are 3 - 14. [object Object]

Your tasks are:

Using for loop iterate over n_cl. Within the loop:

Create KMedoids model with j clusters named model.
Fit the 2-15 columns of data to the model. Watch out that indices in Python start from 0.
Add silhouette score value to the silhouettes list. Remember to pass two parameters: the data used for fitting (the same 3-15 columns) and predicted by model labels.

Visualize the results using lineplot of sns. Pass n_cl as x parameter and silhouettes as y. Do not forget to apply the .show() method to display the plot.

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

Everything was clear?

Section 2. Chapter 4

K-Medoids and the Weather Data

As you can see, there was no such clear peek as in the example. That means that both 3 and 4 clusters may be a good choice!

Let's see what will be the result of using the K-Medoids algorithm for the weather data we used in the previous section. Let's start with defining the optimal number of clusters.

Task

Given cities' average temperatures dataset data. The numerical columns are 3 - 14. [object Object]

Your tasks are:

Using for loop iterate over n_cl. Within the loop:

Create KMedoids model with j clusters named model.
Fit the 2-15 columns of data to the model. Watch out that indices in Python start from 0.
Add silhouette score value to the silhouettes list. Remember to pass two parameters: the data used for fitting (the same 3-15 columns) and predicted by model labels.

Visualize the results using lineplot of sns. Pass n_cl as x parameter and silhouettes as y. Do not forget to apply the .show() method to display the plot.

Task

Given cities' average temperatures dataset data. The numerical columns are 3 - 14. [object Object]

Your tasks are:

Using for loop iterate over n_cl. Within the loop:

Create KMedoids model with j clusters named model.
Fit the 2-15 columns of data to the model. Watch out that indices in Python start from 0.
Add silhouette score value to the silhouettes list. Remember to pass two parameters: the data used for fitting (the same 3-15 columns) and predicted by model labels.

Visualize the results using lineplot of sns. Pass n_cl as x parameter and silhouettes as y. Do not forget to apply the .show() method to display the plot.

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

Everything was clear?

Section 2. Chapter 4

K-Medoids and the Weather Data

As you can see, there was no such clear peek as in the example. That means that both 3 and 4 clusters may be a good choice!

Let's see what will be the result of using the K-Medoids algorithm for the weather data we used in the previous section. Let's start with defining the optimal number of clusters.

Task

Given cities' average temperatures dataset data. The numerical columns are 3 - 14. [object Object]

Your tasks are:

Using for loop iterate over n_cl. Within the loop:

Create KMedoids model with j clusters named model.
Fit the 2-15 columns of data to the model. Watch out that indices in Python start from 0.
Add silhouette score value to the silhouettes list. Remember to pass two parameters: the data used for fitting (the same 3-15 columns) and predicted by model labels.

Visualize the results using lineplot of sns. Pass n_cl as x parameter and silhouettes as y. Do not forget to apply the .show() method to display the plot.

Task

Given cities' average temperatures dataset data. The numerical columns are 3 - 14. [object Object]

Your tasks are:

Using for loop iterate over n_cl. Within the loop:

Create KMedoids model with j clusters named model.
Fit the 2-15 columns of data to the model. Watch out that indices in Python start from 0.
Add silhouette score value to the silhouettes list. Remember to pass two parameters: the data used for fitting (the same 3-15 columns) and predicted by model labels.

Visualize the results using lineplot of sns. Pass n_cl as x parameter and silhouettes as y. Do not forget to apply the .show() method to display the plot.

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

Everything was clear?

As you can see, there was no such clear peek as in the example. That means that both 3 and 4 clusters may be a good choice!

Let's see what will be the result of using the K-Medoids algorithm for the weather data we used in the previous section. Let's start with defining the optimal number of clusters.

Task

Given cities' average temperatures dataset data. The numerical columns are 3 - 14. [object Object]

Your tasks are:

Using for loop iterate over n_cl. Within the loop:

Create KMedoids model with j clusters named model.
Fit the 2-15 columns of data to the model. Watch out that indices in Python start from 0.
Add silhouette score value to the silhouettes list. Remember to pass two parameters: the data used for fitting (the same 3-15 columns) and predicted by model labels.

Visualize the results using lineplot of sns. Pass n_cl as x parameter and silhouettes as y. Do not forget to apply the .show() method to display the plot.

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

Section 2. Chapter 4

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