Apply Clustering Analysis to group the regions with similar average temperatures

All the instructions can be found here

1. Log in to your Tableau Cloud Developer Site.
2. Go to Settings > Extensions.
3. On the Settings page, click the Extensions tab and then scroll to Analytics Extensions.
4. Select "Enable analytics extensions for site" (uncheck by default).
5. Click Create new connection.
6. Select "TabPy" for connection type
7. In Analytics Extension Connection, enter the configuration settings for your analytics service:

Now that we have set up our Tableau Cloud Site to use the TabPy Server that we deployed on Heroku, it is time for us to use it.

1. Download the dataset here

2. Connect to the data a. Log in to your Tableau Cloud Developer Site. b. Open the Connect to the Data page. you can open this page two ways: Home > New > Workbook Explore > New > Workbook c. Select the tab "Files"

3. Create a map of all regions in France. Drag and drop the field "Regions" to Detail on the worksheet.

4. In this new workbook, create a new Calculated Field

5. Use the function SCRIPT_INT

   Tableau can pass code to TabPy through four different functions: SCRIPT_INT, SCRIPT_REAL, SCRIPT_STR and SCRIPT_BOOL to accommodate the different return types.

6. We are now going to apply the Affinity Propagation clustering algorithm to cluster the regions based on similarity temperature for months of the year. You can copy/paste the script below

SCRIPT_INT(
"
import numpy as np
from sklearn.cluster import AffinityPropagation

X = np.column_stack([_arg1, _arg2, _arg3, _arg4, _arg5, _arg6, _arg7, _arg8, _arg9, _arg10, _arg11, _arg12])

db = AffinityPropagation().fit(X)

return db.labels_.tolist()

",SUM([April]),SUM([August]),SUM([December]),SUM([February]),SUM([January]),SUM([July]),SUM([June]),SUM([March]),SUM([May]),SUM([November]),SUM([October]),SUM([September])
)

In this section, we are using OPTIC and K-mean clustering algorithms. The goal is to compare the results of the three clustering algorithms against the climate zones of France. You can find the climate zones of France here.

1. Create a new calculated field.
2. Copy/Paste this script for OPTIC clustering.

SCRIPT_INT(
"
import numpy as np
from sklearn.cluster import OPTICS

X = np.column_stack([_arg1, _arg2, _arg3, _arg4, _arg5, _arg6, _arg7, _arg8, _arg9, _arg10, _arg11, _arg12])

db = OPTICS().fit(X)

return db.labels_.tolist()

",SUM([April]),SUM([August]),SUM([December]),SUM([February]),SUM([January]),SUM([July]),SUM([June]),SUM([March]),SUM([May]),SUM([November]),SUM([October]),SUM([September])


)

3. Create another calculated field for k-mean clustering. For k-mean clustering you can specify the number of clusters, we are going to set it to 5.

SCRIPT_INT(
"
import numpy as np
from sklearn.cluster import KMeans

X = np.column_stack([_arg1, _arg2, _arg3, _arg4, _arg5, _arg6, _arg7, _arg8, _arg9, _arg10, _arg11, _arg12])

db = KMeans(n_clusters=5).fit(X)

return db.labels_.tolist()

",SUM([April]),SUM([August]),SUM([December]),SUM([February]),SUM([January]),SUM([July]),SUM([June]),SUM([March]),SUM([May]),SUM([November]),SUM([October]),SUM([September])


)