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  3. Market Basket Analysis in Python
  • 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.

  • 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.

  • 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.

  • 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.

Initializing

Exercise

Promoting ebooks with conviction

In the previous exercise, we defined a function to compute conviction. We were asked to apply that function to all two-book permutations of the goodreads-10k dataset. In this exercise, we'll test the function by applying it to the three most popular books, which we used in earlier exercises: The Hunger Games, Harry Potter, and Twilight.

The function has been defined for you and is available as conviction. Recall that it takes an antecedent and a consequent as its two arguments. Additionally, the columns of the books DataFrame from earlier exercises are available as three separate DataFrames: potter, twilight, and hunger.

Instructions

100 XP
  • Compute conviction for {Twilight} \(\rightarrow\) {Potter} and {Potter} \(\rightarrow\) {Twilight}.
  • Compute conviction for {Twilight} \(\rightarrow\) {Hunger} and {Hunger} \(\rightarrow\) {Twilight}.
  • Compute conviction for {Potter} \(\rightarrow\) {Hunger} and {Hunger} \(\rightarrow\) {Potter}.