Customer Segmentation Pipeline

A scalable ML pipeline for customer segmentation using K-Means clustering. Includes data loading, feature scaling, clustering, and visualization — ready to run on sample data.

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Pipeline Stages

data source

Data Loader

Loads the Iris dataset (150 samples, 4 features)

preprocessing

Standard Scaler

Normalizes features to zero mean, unit variance

model

K-Means

Partitions data into 3 clusters using k-means++ initialization

evaluation

Cluster Plot

2D PCA scatter plot colored by cluster assignment

Overview

This pipeline takes raw customer data through a complete clustering workflow: first normalizing features with StandardScaler to ensure each dimension contributes equally, then applying K-Means to discover natural customer segments, and finally visualizing the results in a scatter plot with PCA-reduced dimensions.

Why This Pipeline Works

StandardScaler is essential before K-Means because the algorithm uses Euclidean distance — without scaling, features with larger ranges dominate the distance calculation. K-Means with k-means++ initialization converges faster and produces more stable clusters than random initialization.

Expected Output

The pipeline produces cluster assignments for each customer, a 2D scatter plot colored by cluster, and internal validation metrics (Silhouette Score, Calinski-Harabasz Index, Davies-Bouldin Index) that quantify how well-separated the clusters are.

Evaluation Metrics

Silhouette Score

measures how similar each point is to its own cluster vs. the nearest cluster (-1 to 1, higher is better)

Calinski-Harabasz Index

ratio of between-cluster to within-cluster variance (higher is better)

Davies-Bouldin Index

average similarity between each cluster and its most similar one (lower is better)

Inertia

sum of squared distances to cluster centers (lower indicates tighter clusters)

Related Topics

customer segmentation pipelineK-Means clustering workflowML pipeline for customer segmentationpreprocessing and clustering workflowscalable ML pipelineautomated customer grouping

Ready to try it?

Load this blueprint into the interactive pipeline editor and run it on sample data — no setup required.

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