Customer segmentation pattern in Data Analysis Python - Step-by-Step Execution

Concept Flow - Customer segmentation pattern

Load customer data

↓

Select features for segmentation

↓

Apply clustering algorithm

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Assign cluster labels to customers

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Analyze and visualize segments

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Use segments for targeted actions

This flow shows how customer data is loaded, features are selected, clustering groups customers, and segments are analyzed for business use.

Execution Sample

Data Analysis Python

import pandas as pd
from sklearn.cluster import KMeans

# Load data
customers = pd.DataFrame({'Age':[25,45,35,23,52],'Income':[50000,64000,58000,45000,72000]})

# Cluster
kmeans = KMeans(n_clusters=2, random_state=0).fit(customers)
customers['Segment'] = kmeans.labels_

This code loads a small customer dataset, applies KMeans clustering with 2 groups, and assigns segment labels.

Execution Table

Step	Action	Data Shape	Cluster Centers	Segment Labels
1	Load customer data	(5, 2)	N/A	N/A
2	Initialize KMeans with 2 clusters	(5, 2)	N/A	N/A
3	Fit KMeans to data	(5, 2)	[[24.0, 47500.0], [44.0, 64666.66666667]]	N/A
4	Assign cluster labels	(5, 3)	[[24.0, 47500.0], [44.0, 64666.66666667]]	[0, 1, 1, 0, 1]
5	Final segmented data	(5, 3)	[[24.0, 47500.0], [44.0, 64666.66666667]]	[0, 1, 1, 0, 1]

💡 Clustering complete, segments assigned to all customers.

Variable Tracker

Variable	Start	After Step 3	After Step 4	Final
customers	Empty	(5, 2) DataFrame	(5, 3) DataFrame	(5, 3) DataFrame with Segment
kmeans.cluster_centers_	N/A	[[24.0, 47500.0], [44.0, 64666.66666667]]	[[24.0, 47500.0], [44.0, 64666.66666667]]	[[24.0, 47500.0], [44.0, 64666.66666667]]
kmeans.labels_	N/A	N/A	[0, 1, 1, 0, 1]	[0, 1, 1, 0, 1]

Key Moments - 3 Insights

Why do the cluster centers have values like [24.0, 47500.0]?

These centers represent the average Age and Income of customers in each cluster, calculated during fitting (see Step 3 in execution_table).

Why are segment labels assigned only after fitting the model?

Labels depend on the clustering result, so they are only available after the model learns clusters from data (see Step 4).

What does the 'Segment' column in the final DataFrame represent?

It shows which cluster each customer belongs to, enabling segmentation for analysis or marketing (see Step 5).

Visual Quiz - 3 Questions

Look at the execution_table at Step 4, what is the segment label for the customer with Age 35?

A 1

B 0

C 2

D N/A

💡 Hint

Check the Segment Labels column at Step 4; the third label corresponds to the third customer (Age 35).

At which step do we first see the cluster centers calculated?

A Step 2

B Step 4

C Step 3

D Step 5

💡 Hint

Look at the Cluster Centers column; centers appear after fitting the model.

If we change n_clusters to 3, what will change in the execution_table?

A Segment labels will remain the same

B Number of cluster centers will be 3

C Data shape will change

D Cluster centers will be empty

💡 Hint

Number of clusters controls how many centers are computed (see Step 3 Cluster Centers).

Concept Snapshot

Customer Segmentation Pattern:
- Load customer data with features (e.g., Age, Income)
- Use clustering (e.g., KMeans) to group customers
- Assign cluster labels as segments
- Analyze segments for targeted marketing
- Key: cluster centers represent group averages

Full Transcript

Customer segmentation groups customers by similar traits. We load data, select features like Age and Income, then apply KMeans clustering. The algorithm finds cluster centers representing average customer profiles. Each customer gets a segment label showing their group. This helps businesses target marketing or services. The execution table shows steps from loading data to assigning segments. Variable tracking shows how data and model outputs change. Key moments clarify cluster centers and label assignment. Quizzes test understanding of labels, cluster centers, and effect of changing cluster count.