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Market Basket Analysis in R

Explore association rules in market basket analysis with R by analyzing retail data and creating movie recommendations.

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4 Hours16 Videos60 Exercises3,219 Learners4700 XPMarketing Analytics Track

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Course Description

Last time you were at the supermarket, what was in your shopping basket? Was there a connection between the products you purchased, like spaghetti and tomatoes or ham and pineapple? Whether online or offline, retailers use information from millions of customer’s baskets to analyze associations between items and extract insights using association rules. To help you quantify the degree of association between items you’ll use market basket analysis to uncover unseen connections and visualize relevant and insightful rules. You’ll then get to practice what you’ve learned on a movie dataset, as you predict which movies are watched together to create personalized movie recommendations for users.

  1. 1

    Introduction to Market Basket Analysis


    What’s in your basket? In this first chapter, you’ll learn how market basket analysis (MBA) can be used to look into baskets and dig into itemsets to better understand customers and predict their needs. Using tidyverse and dplyr you’ll discover how many baskets can be created from a given set of items, and learn the power of using market basket analysis for online shopping, offline shopping, and use cases beyond retail.

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    Market basket introduction
    50 xp
    Baskets and items
    50 xp
    Single basket
    100 xp
    What's in the basket?
    100 xp
    Item combinations
    50 xp
    Number of possible baskets
    100 xp
    Subsets and supersets
    50 xp
    Plot number of possible baskets
    100 xp
    What is market basket analysis ?
    50 xp
    Two baskets
    100 xp
    Multiple baskets
    100 xp
    Looking at specific items
    100 xp
  2. 2

    Metrics & Techniques in Market Basket Analysis

    In this chapter, you’ll convert transactional datasets to a basket format, ready for analysis using the Apriori algorithm. You’ll then be introduced to the three main metrics for market basket analysis: support, confidence, and lift, before getting hands-on with the Apriori algorithm to extract rules from a transactional dataset. Lastly, You explore how the arules package is used for market basket analysis to retrieve basket rules and to help you find the most informative and relevant rules.

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  3. 3

    Visualization in Market Basket Analysis

    Let’s get visual. In this chapter, you’ll visually inspect the set of rules you have previously extracted. Visualizations in market basket analysis are vital given that often you are dealing with large sets of extracted rules. You’ll use the arulesViz package to create barplots, scatterplots, and graphs to visualize your sets of inferred rules. You’ll then turn sets of plots into interactive plots, making it is easier to drill into the mined association rules.

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  4. 4

    Case Study: Market basket with Movies

    We’re going to the movies. In this final chapter, you’ll apply everything you’ve learned as you work with a movie dataset. Using market basket analysis you’ll turn this dataset into a movie recommendation system, using information from movie transactions to understand and predict what your audience might want to watch next.

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In the following tracks

Marketing Analytics


AAN94Adel NehmeanneleenAnneleen Beckers
Christopher Bruffaerts Headshot

Christopher Bruffaerts


Christopher is a Data Scientist with a wealth of industry experience in different sectors from banking, telecommunications, energy, and education. He's passionate about teaching, learning, and identifying the best teaching style for any given audience. In both his private and professional life, he's a data-driven person and always knows how to use it to make better decisions.
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