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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.
Introduction to Market Basket AnalysisFree
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.Market basket introduction50 xpBaskets and items50 xpSingle basket100 xpWhat's in the basket?100 xpItem combinations50 xpNumber of possible baskets100 xpSubsets and supersets50 xpPlot number of possible baskets100 xpWhat is market basket analysis ?50 xpTwo baskets100 xpMultiple baskets100 xpLooking at specific items100 xp
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.Transactional data50 xpTransactionalizing the online data frame100 xpInspector retail100 xpDensity of the item matrix?50 xpMetrics in market basket analysis50 xpThe support measure100 xpConfidence and lift measures100 xpChanging the appearance of rules100 xpThe apriori algorithm50 xpApriori principle50 xpLet's go shopping for rules100 xpParameters of the apriori100 xp“If this then that” with the apriori50 xpPlaying with the appearance100 xpRedundant rules100 xpInterpretation of rules50 xp
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.Let's see what's in the basket50 xpWhat's in the basket?100 xpWhat's most popular ?50 xpGetting fancy with the visuals100 xpVisualizing metrics50 xpScatterplots100 xpArulesViz plots100 xpBringing rules to life50 xpFrom rules to graph based visualizations50 xpPlaying with graphs100 xpUnderstanding the graph50 xpPortability of your graph100 xpAlternative rule plots50 xpGroup matrix based visualizations100 xpParallel coordinates plots100 xpMastering the RuleExplorer50 xp
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.Recap on transactions50 xpGettting familiar with the MovieLens dataset100 xpMovie transactions100 xpFrequency plots100 xpMining association rules50 xpPopularity of movies100 xpPicking the right movie parameters100 xpExtracting movie rules100 xpVisualizing transactions and rules50 xpWhere is Pulp Fiction?50 xpVisualizing movie rules100 xpOur favorite movies as a graph100 xpMaking the most of market basket analysis50 xpWhat influenced Pulp Fiction?100 xpWhat did Pulp Fiction influence?100 xpUse your market basket skills!50 xp
In the following tracksMarketing Analytics
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.