中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
您将学到什么
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.
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可分享的证书
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最近已更新！
September 2025
作业
7 项作业
授课语言：英语（English）
了解顶级公司的员工如何掌握热门技能
该课程共有2个模块
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.
涵盖的内容
10个视频4个作业
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.
涵盖的内容
4个视频3个作业
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