Conteúdo do Curso

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

Comparing the Dynamics

That's an interesting result! The yearly average temperatures across clusters significantly differ for 3 of them (47.3, 60.9, and 79.24). It seems like a good split.

Now let's visualize the monthly dynamics of average temperatures across clusters, and compare the result with the 5 clusters by the K-Means algorithm. The respective line plot is below.

Tarefa

Visualize the monthly temperature dynamics across clusters. Follow the next steps:

Import KMedoids function from sklearn_extra.cluster.
Create a KMedoids object named model with 4 clusters.
Fit the 3-15 columns (these are not indices, but positions) of data to model.
Add the 'prediction' column to data with predicted by model labels.
Calculate the monthly averages using data and save the result within the d DataFrame:

Group the observations by the 'prediction' column.
Calculate the mean values.
Stack the columns into indices (already done).
Reset the indices.

Assign ['Group', 'Month', 'Temp'] as columns names of d.
Build lineplot with 'Month' on the x-axis, 'Temp' on the y-axis for each 'Group' of d DataFrame (i.e. separate line and color for each 'Group').

Tarefa

Visualize the monthly temperature dynamics across clusters. Follow the next steps:

Import KMedoids function from sklearn_extra.cluster.
Create a KMedoids object named model with 4 clusters.
Fit the 3-15 columns (these are not indices, but positions) of data to model.
Add the 'prediction' column to data with predicted by model labels.
Calculate the monthly averages using data and save the result within the d DataFrame:

Group the observations by the 'prediction' column.
Calculate the mean values.
Stack the columns into indices (already done).
Reset the indices.

Assign ['Group', 'Month', 'Temp'] as columns names of d.
Build lineplot with 'Month' on the x-axis, 'Temp' on the y-axis for each 'Group' of d DataFrame (i.e. separate line and color for each 'Group').

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Tudo estava claro?

Seção 2. Capítulo 6

Comparing the Dynamics

That's an interesting result! The yearly average temperatures across clusters significantly differ for 3 of them (47.3, 60.9, and 79.24). It seems like a good split.

Now let's visualize the monthly dynamics of average temperatures across clusters, and compare the result with the 5 clusters by the K-Means algorithm. The respective line plot is below.

Tarefa

Visualize the monthly temperature dynamics across clusters. Follow the next steps:

Import KMedoids function from sklearn_extra.cluster.
Create a KMedoids object named model with 4 clusters.
Fit the 3-15 columns (these are not indices, but positions) of data to model.
Add the 'prediction' column to data with predicted by model labels.
Calculate the monthly averages using data and save the result within the d DataFrame:

Group the observations by the 'prediction' column.
Calculate the mean values.
Stack the columns into indices (already done).
Reset the indices.

Assign ['Group', 'Month', 'Temp'] as columns names of d.
Build lineplot with 'Month' on the x-axis, 'Temp' on the y-axis for each 'Group' of d DataFrame (i.e. separate line and color for each 'Group').

Tarefa

Visualize the monthly temperature dynamics across clusters. Follow the next steps:

Import KMedoids function from sklearn_extra.cluster.
Create a KMedoids object named model with 4 clusters.
Fit the 3-15 columns (these are not indices, but positions) of data to model.
Add the 'prediction' column to data with predicted by model labels.
Calculate the monthly averages using data and save the result within the d DataFrame:

Group the observations by the 'prediction' column.
Calculate the mean values.
Stack the columns into indices (already done).
Reset the indices.

Assign ['Group', 'Month', 'Temp'] as columns names of d.
Build lineplot with 'Month' on the x-axis, 'Temp' on the y-axis for each 'Group' of d DataFrame (i.e. separate line and color for each 'Group').

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Seção 2. Capítulo 6

Comparing the Dynamics

That's an interesting result! The yearly average temperatures across clusters significantly differ for 3 of them (47.3, 60.9, and 79.24). It seems like a good split.

Now let's visualize the monthly dynamics of average temperatures across clusters, and compare the result with the 5 clusters by the K-Means algorithm. The respective line plot is below.

Tarefa

Visualize the monthly temperature dynamics across clusters. Follow the next steps:

Import KMedoids function from sklearn_extra.cluster.
Create a KMedoids object named model with 4 clusters.
Fit the 3-15 columns (these are not indices, but positions) of data to model.
Add the 'prediction' column to data with predicted by model labels.
Calculate the monthly averages using data and save the result within the d DataFrame:

Group the observations by the 'prediction' column.
Calculate the mean values.
Stack the columns into indices (already done).
Reset the indices.

Assign ['Group', 'Month', 'Temp'] as columns names of d.
Build lineplot with 'Month' on the x-axis, 'Temp' on the y-axis for each 'Group' of d DataFrame (i.e. separate line and color for each 'Group').

Tarefa

Visualize the monthly temperature dynamics across clusters. Follow the next steps:

Import KMedoids function from sklearn_extra.cluster.
Create a KMedoids object named model with 4 clusters.
Fit the 3-15 columns (these are not indices, but positions) of data to model.
Add the 'prediction' column to data with predicted by model labels.
Calculate the monthly averages using data and save the result within the d DataFrame:

Group the observations by the 'prediction' column.
Calculate the mean values.
Stack the columns into indices (already done).
Reset the indices.

Assign ['Group', 'Month', 'Temp'] as columns names of d.
Build lineplot with 'Month' on the x-axis, 'Temp' on the y-axis for each 'Group' of d DataFrame (i.e. separate line and color for each 'Group').

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

That's an interesting result! The yearly average temperatures across clusters significantly differ for 3 of them (47.3, 60.9, and 79.24). It seems like a good split.

Now let's visualize the monthly dynamics of average temperatures across clusters, and compare the result with the 5 clusters by the K-Means algorithm. The respective line plot is below.

Tarefa

Visualize the monthly temperature dynamics across clusters. Follow the next steps:

Import KMedoids function from sklearn_extra.cluster.
Create a KMedoids object named model with 4 clusters.
Fit the 3-15 columns (these are not indices, but positions) of data to model.
Add the 'prediction' column to data with predicted by model labels.
Calculate the monthly averages using data and save the result within the d DataFrame:

Group the observations by the 'prediction' column.
Calculate the mean values.
Stack the columns into indices (already done).
Reset the indices.

Assign ['Group', 'Month', 'Temp'] as columns names of d.
Build lineplot with 'Month' on the x-axis, 'Temp' on the y-axis for each 'Group' of d DataFrame (i.e. separate line and color for each 'Group').

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Seção 2. Capítulo 6

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo