Customer Segmentation with K-Means: Model & Visualize

Instructor: EDUCBA

Access provided by BAC Education Group

2 modules

Gain insight into a topic and learn the fundamentals.

4 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Preprocess and organize customer data for clustering analysis in Python.
Build and apply K-Means models to segment customers effectively.
Visualize clusters and interpret insights for strategic business decisions.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

7 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

There are 2 modules in this course

This practical course equips learners with the analytical skills to explore, model, and visualize customer shopping behavior using Python and K-Means clustering. Through structured modules, learners will prepare real-world customer data, construct meaningful visualizations, analyze variable relationships, and evaluate clustering outcomes to derive actionable business insights.

Starting with data preprocessing and environment setup, learners will organize datasets and construct various statistical charts, including pie charts, histograms, and violin plots, to interpret customer attributes. Building on this foundation, the course guides learners through correlation analysis, scaling, and model development using the K-Means algorithm. Finally, learners will visualize customer clusters and assess shopping behavior to support strategic segmentation and personalized marketing decisions. By the end of this course, learners will be able to apply unsupervised machine learning techniques to segment customers and formulate data-driven business insights from complex shopping datasets.

This module introduces learners to the foundational stages of customer data analysis using Python. Participants will explore the end-to-end setup of the project environment, import essential libraries, and apply preprocessing techniques to prepare data for analysis. Through hands-on visualizations such as pie charts, histograms, violin plots, and pair plots, learners will interpret univariate and multivariate data distributions. The module concludes with a comparative gender-based exploration of spending behavior, enabling learners to extract meaningful insights from visual patterns.

What's included

10 videos4 assignments

10 videosTotal 79 minutes

Introduction of Project5 minutes
Import Libraries8 minutes
Data Preprocessing14 minutes
Pie chart8 minutes
Histogram7 minutes
Violin plot8 minutes
Distribution Plot Analysis8 minutes
Pair plot and Female Data Analysis5 minutes
Male Data Analysis7 minutes
Male Data Analysis Continue4 minutes

4 assignmentsTotal 60 minutes

Graded - Data Exploration and Visualization30 minutes
Getting Started with Data Analysis10 minutes
Univariate and Comparative Visualizations10 minutes
Advanced Visual Insights10 minutes

This module focuses on uncovering meaningful patterns in customer data through correlation analysis and predictive modeling. Learners will explore how to interpret relationships between variables using heatmaps, prepare data for clustering using K-Means, and visualize the resulting clusters to extract actionable insights. The module culminates in segmenting customers based on behavioral and financial attributes to support business decision-making.