Customer Segmentation with K-Means: Model & Visualize

位教师：EDUCBA

访问权限由 Coursera Learning Team 提供

2个模块

深入了解一个主题并学习基础知识。

5 小时完成

灵活的计划

自行安排学习进度

2个模块

深入了解一个主题并学习基础知识。

5 小时完成

灵活的计划

自行安排学习进度

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.

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

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

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有2个模块

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.

涵盖的内容

10个视频4个作业

10个视频总计80分钟

Introduction of Project 5分钟
Import Libraries 9分钟
Data Preprocessing 15分钟
Pie chart 9分钟
Histogram 7分钟
Violin plot 9分钟
Distribution Plot Analysis 8分钟
Pair plot and Female Data Analysis 6分钟
Male Data Analysis 8分钟
Male Data Analysis Continue 4分钟

4个作业总计60分钟

Graded - Data Exploration and Visualization 30分钟
Getting Started with Data Analysis 10分钟
Univariate and Comparative Visualizations 10分钟
Advanced Visual Insights 10分钟

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.