Customer Segmentation using K-Means Clustering in R
10 ratings

Understand the intuition behind the K-Means Clustering algorithm
Create plots of the customer features
Create plots of the distinct customer segments based on features
10 ratings
Understand the intuition behind the K-Means Clustering algorithm
Create plots of the customer features
Create plots of the distinct customer segments based on features
Welcome to this project-based course, Customer Segmentation using K-Means Clustering in R. In this project, you will learn how to perform customer market segmentation on mall customers data using different R packages. By the end of this 2-and-a-half-hour long project, you will understand how to get the mall customers data into your RStudio workspace and explore the data. By extension, you will learn how to use the ggplot2 package to render beautiful plots of the data. Also, you will learn how to get the optimal number of clusters for the customers' segments and use K-Means to create distinct groups of customers based on their characteristics. Finally, you will learn how to use the R markdown file to organise your work and how to knit your code into an HTML document for publishing. Although you do not need to be a data analyst expert or data scientist to succeed in this guided project, it requires a basic knowledge of using R, especially writing R syntaxes. Therefore, to complete this project, you must have prior experience with using R. If you are not familiar with working with using R, please go ahead to complete my previous project titled: “Getting Started with R”. It will hand you the needed knowledge to go ahead with this project on Customer Segmentation. However, if you are comfortable with working with R, please join me on this beautiful ride! Let’s get our hands dirty!
clustering
Ggplot2
K-Means Clustering
PCA
unsupervised machine learning
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Getting Started
Import and Explore the Data
Data Visualization - Part One
Data Visualization - Part Two
Understand the concept of K-Means
Determine the number of Clusters
K-Means Clustering
Principal Component Analysis
Plot the K-Means Segments
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
by AB
Dec 25, 2022Thanks for the provided readings and the instruction! It was also great to learn additional tools in data visualization.
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