Customer Segmentation using K-Means Clustering in R

от партнера
Coursera Project Network
В этом Проект с консультациями вы:

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

Clock2.5 hours
IntermediateУчащийся среднего уровня
CloudЗагрузка не требуется
VideoВидео на разделенном экране
Comment DotsАнглийский
LaptopТолько для ПК

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!

Навыки, которые вы получите

  • clustering
  • Ggplot2
  • K-Means Clustering
  • PCA
  • unsupervised machine learning

Будете учиться пошагово

На видео, которое откроется рядом с рабочей областью, преподаватель объяснит эти шаги:

  1. Getting Started

  2. Import and Explore the Data

  3. Data Visualization - Part One

  4. Data Visualization - Part Two

  5. Understand the concept of K-Means

  6. Determine the number of Clusters

  7. K-Means Clustering

  8. Principal Component Analysis

  9. Plot the K-Means Segments

Как устроены проекты с консультациями

Ваше рабочее пространство — это облачный рабочий стол в браузере. Ничего не нужно загружать.

На разделенном экране видео преподаватель предоставляет пошаговые

Часто задаваемые вопросы

Часто задаваемые вопросы

Остались вопросы? Посетите Центр поддержки учащихся.