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What do Amazon product recommendations and Netflix movie suggestions have in common? They both rely on Market Basket Analysis, which is a powerful tool for translating vast amounts of customer transaction and viewing data into simple rules for product promotion and recommendation. In this course, you’ll learn how to perform Market Basket Analysis using the Apriori algorithm, standard and custom metrics, association rules, aggregation and pruning, and visualization. You’ll then reinforce your new skills through interactive exercises, building recommendations for a small grocery store, a library, an e-book seller, a novelty gift retailer, and a movie streaming service. In the process, you’ll uncover hidden insights to improve recommendations for customers.
Introduction to Market Basket AnalysisFree
In this chapter, you’ll learn the basics of Market Basket Analysis: association rules, metrics, and pruning. You’ll then apply these concepts to help a small grocery store improve its promotional and product placement efforts.What is market basket analysis?50 xpThe basics of market basket analysis50 xpCross-selling products100 xpIdentifying association rules50 xpMultiple antecedents and consequents100 xpPreparing data for market basket analysis100 xpGenerating association rules100 xpThe simplest metric50 xpOne-hot encoding transaction data100 xpComputing the support metric100 xp
Association rules tell us that two or more items are related. Metrics allow us to quantify the usefulness of those relationships. In this chapter, you’ll apply six metrics to evaluate association rules: supply, confidence, lift, conviction, leverage, and Zhang's metric. You’ll then use association rules and metrics to assist a library and an e-book seller.Confidence and lift50 xpRecommending books with support100 xpRefining support with confidence100 xpFurther refinement with lift100 xpLeverage and conviction50 xpLift versus leverage100 xpComputing conviction100 xpComputing conviction with a function100 xpPromoting ebooks with conviction100 xpAssociation and dissociation50 xpComputing association and dissociation100 xpDefining Zhang's metric100 xpApplying Zhang's metric100 xpAdvanced rules50 xpFiltering with support and conviction100 xpUsing multi-metric filtering to cross-promote books100 xp
Aggregation and Pruning
The fundamental problem of Market Basket Analysis is determining how to translate vast amounts of customer decisions into a small number of useful rules. This process typically starts with the application of the Apriori algorithm and involves the use of additional strategies, such as pruning and aggregation. In this chapter, you’ll learn how to use these methods and will ultimately apply them in exercises where you assist a retailer in selecting a physical store layout and performing product cross-promotions.Aggregation50 xpPerforming aggregation100 xpDefining an aggregation function100 xpThe Apriori algorithm50 xpPruning and Apriori100 xpIdentifying frequent itemsets with Apriori100 xpSelecting a support threshold100 xpBasic Apriori results pruning50 xpGenerating association rules100 xpPruning with lift100 xpPruning with confidence100 xpAdvanced Apriori results pruning50 xpAggregation and filtering100 xpApplying Zhang's rule100 xpAdvanced filtering with multiple metrics100 xp
In this final chapter, you’ll learn how visualizations are used to guide the pruning process and summarize final results, which will typically take the form of itemsets or rules. You’ll master the three most useful visualizations -- heatmaps, scatterplots, and parallel coordinates plots – and will apply them to assist a movie streaming service.Heatmaps50 xpVisualizing itemset support100 xpHeatmaps with lift100 xpInterpreting heatmaps50 xpScatterplots50 xpPruning with scatterplots100 xpOptimality of the support-confidence border100 xpParallel coordinates plot50 xpUsing parallel coordinates to visualize rules100 xpRefining a parallel coordinates plot100 xpCongratulations!50 xp
In the following tracksMarketing Analytics
PrerequisitesData Manipulation with pandas
Isaiah Hull is a visiting associate professor of finance at BI Norwegian Business School and the author of Machine Learning for Economics and Finance in TensorFlow 2. He holds a PhD in economics from Boston College and conducts research on computational economics, machine learning, and quantum computing.