Зміст курсу

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

Mean 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.

Завдання

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

Follow the next steps:

Create a KMedoids model with 4 clusters named model.
Fit the 3-15 (these are positions, not indices) columns of data to model.
Add the 'prediction' column to data with predicted by model labels.
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.

Завдання

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

Follow the next steps:

Create a KMedoids model with 4 clusters named model.
Fit the 3-15 (these are positions, not indices) columns of data to model.
Add the 'prediction' column to data with predicted by model labels.
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.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Секція 2. Розділ 5

Mean 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.

Завдання

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

Follow the next steps:

Create a KMedoids model with 4 clusters named model.
Fit the 3-15 (these are positions, not indices) columns of data to model.
Add the 'prediction' column to data with predicted by model labels.
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.

Завдання

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

Follow the next steps:

Create a KMedoids model with 4 clusters named model.
Fit the 3-15 (these are positions, not indices) columns of data to model.
Add the 'prediction' column to data with predicted by model labels.
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.

Все було зрозуміло?

Секція 2. Розділ 5

Mean 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.

Завдання

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

Follow the next steps:

Create a KMedoids model with 4 clusters named model.
Fit the 3-15 (these are positions, not indices) columns of data to model.
Add the 'prediction' column to data with predicted by model labels.
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.

Завдання

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

Follow the next steps:

Create a KMedoids model with 4 clusters named model.
Fit the 3-15 (these are positions, not indices) columns of data to model.
Add the 'prediction' column to data with predicted by model labels.
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.

Все було зрозуміло?

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.

Завдання

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

Follow the next steps:

Create a KMedoids model with 4 clusters named model.
Fit the 3-15 (these are positions, not indices) columns of data to model.
Add the 'prediction' column to data with predicted by model labels.
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.

Секція 2. Розділ 5