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.
Customer Segmentation with K-Means: Model & Visualize
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3 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
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Recently updated!
September 2025
Assessments
7 assignments
Taught in English
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There are 2 modules in this course
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
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.
What's included
4 videos3 assignments
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