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Market Basket Analysis in Python

Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.

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4 Hours15 Videos52 Exercises8,427 Learners4350 XPMarketing Analytics Track

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Course Description

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.
  1. 1

    Introduction to Market Basket Analysis

    Free

    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.

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    What is market basket analysis?
    50 xp
    The basics of market basket analysis
    50 xp
    Cross-selling products
    100 xp
    Identifying association rules
    50 xp
    Multiple antecedents and consequents
    100 xp
    Preparing data for market basket analysis
    100 xp
    Generating association rules
    100 xp
    The simplest metric
    50 xp
    One-hot encoding transaction data
    100 xp
    Computing the support metric
    100 xp
  2. 2

    Association Rules

    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.

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  3. 3

    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.

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  4. 4

    Visualizing Rules

    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.

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In the following tracks

Marketing Analytics

Collaborators

AAN94
Adel Nehme
amy-4121b590-cc52-442a-9779-03eb58089e08
Amy Peterson
Isaiah Hull Headshot

Isaiah Hull

Economist

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.
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