How do you help someone make sense of a clustering result?
A Python data project that groups customer records and presents summaries and charts for exploring the resulting segments.
Data exploration / GitHub project · demo link
ClusterMap.
A customer-segmentation experiment that makes groups easier to explore.
PythonClusteringPandasData visualization
INTERACTIVE WALKTHROUGH / 01
Choose a control and see how the system’s ideas fit together.
clustermap / interactive studyLOCAL PREVIEW
CUSTOMER SEGMENTS / FEATURE SPACE
FEATURE BRepeat purchase
75 SAMPLE POINTSIllustrative points and labels · the chart is an interaction concept, not a live data set.
Make the system’s logic visible.
Keep the groups close to their visual summaries. Let the viewer focus a segment and compare how the groups sit in feature space.
A CLOSER LOOK / 04From the first input
From the first input
to a useful outcome.
This sequence summarizes the project’s core idea. The interactive panel is a local visual study; use the linked repository or live service for the implemented project.
01 →
Prepare
Represent customer records as numeric features that can be compared.
02 →
Group
Apply clustering to find records with similar feature patterns.
03 →
Explore
Use charts and summaries to make the segments easier to reason about.
KEEP EXPLORING / 05Read the source.