Cluster Analysis in Python

Зміст курсу

Cluster Analysis in Python

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

Weather Data: Complete and Ward Linkages

The last chart was good, but if you remember the K-Means and K-Medoids algorithms results, you may remember that there was at least one more line that unlike all the others goes downwards close to July. The average linkage in hierarchical clustering didn't catch that dynamic.

We saw that for complete and ward linkages there is sense to consider 4 clusters. Let's find out will they catch that?

Завдання

Swipe to start coding

Table

Import numpy with np alias.
Iterate over the linkages list. At each step:

Create a hierarchical clustering model with 4 clusters and method j named model.
Fit the numerical data of temp and predict the labels. Add predicted labels as the 'prediction' column to temp.
Create a temp_res DataFrame with monthly averages for each group. To do it group the values of temp by the 'prediction' column, calculate the mean, and then apply the .stack() method.
Add column 'method' to temp_res DataFrame with value j being repeated the number of rows in temp_res times.
Merge res and temp_res dataframes using .concat function of pd.

Reassign the column names of res to ['Group', 'Month', 'Temp', "Method"].
Within the FacetGrid function set the col parameter to 'Method'. This will build a separate chart for each value of the 'Method' column.
Within the .map function set the seaborn line plot function as the first parameter.

Рішення

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

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 3. Розділ 7

toggle bottom row

Weather Data: Complete and Ward Linkages

The last chart was good, but if you remember the K-Means and K-Medoids algorithms results, you may remember that there was at least one more line that unlike all the others goes downwards close to July. The average linkage in hierarchical clustering didn't catch that dynamic.

We saw that for complete and ward linkages there is sense to consider 4 clusters. Let's find out will they catch that?

Завдання

Swipe to start coding

Table

Import numpy with np alias.
Iterate over the linkages list. At each step:

Create a hierarchical clustering model with 4 clusters and method j named model.
Fit the numerical data of temp and predict the labels. Add predicted labels as the 'prediction' column to temp.
Create a temp_res DataFrame with monthly averages for each group. To do it group the values of temp by the 'prediction' column, calculate the mean, and then apply the .stack() method.
Add column 'method' to temp_res DataFrame with value j being repeated the number of rows in temp_res times.
Merge res and temp_res dataframes using .concat function of pd.

Reassign the column names of res to ['Group', 'Month', 'Temp', "Method"].
Within the FacetGrid function set the col parameter to 'Method'. This will build a separate chart for each value of the 'Method' column.
Within the .map function set the seaborn line plot function as the first parameter.

Рішення

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

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 3. Розділ 7

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

some-alt