Itemset-support visualiseren

Een content-streamingstart-up heeft je benaderd voor advies. Om licentiekosten laag te houden, willen ze een compacte catalogus samenstellen met films die allemaal dezelfde doelgroep aanspreken. Hun aanbod is kleiner dan dat van de grote spelers, maar ze kunnen wel een lage abonnementsprijs bieden.

Je besluit voor dit project de MovieLens-gegevens en een heatmap te gebruiken. Met een simpele, op support gebaseerde heatmap kun je titels vinden die vaak samen met andere titels voorkomen. De one-hot-encoded gegevens staan in de DataFrame onehot. Daarnaast is pandas beschikbaar als pd, seaborn als sns, en zijn apriori() en association_rules() al geïmporteerd.

Deze oefening maakt deel uit van de cursus

Market Basket Analysis in Python

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# Compute frequent itemsets using a minimum support of 0.07
frequent_itemsets = apriori(onehot, min_support = ____, 
                            use_colnames = True, max_len = 2)

# Compute the association rules
rules = association_rules(____, metric = 'support', 
                          min_threshold = 0.0)

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Deze oefening maakt deel uit van de cursus

Market Basket Analysis in Python

SkillTag.level.intermediateSkillTag.label

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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.

Exercise 1: What is market basket analysis?Exercise 2: The basics of market basket analysis Exercise 3: Cross-selling products Exercise 4: Identifying association rules Exercise 5: Multiple antecedents and consequents Exercise 6: Preparing data for market basket analysis Exercise 7: Generating association rules Exercise 8: The simplest metric Exercise 9: One-hot encoding transaction data Exercise 10: Computing the support metric

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.

Exercise 1: Confidence and lift Exercise 2: Recommending books with support Exercise 3: Refining support with confidence Exercise 4: Further refinement with lift Exercise 5: Leverage and conviction Exercise 6: Lift versus leverage Exercise 7: Computing conviction Exercise 8: Computing conviction with a function Exercise 9: Promoting ebooks with conviction Exercise 10: Association and dissociation Exercise 11: Computing association and dissociation Exercise 12: Defining Zhang's metric Exercise 13: Applying Zhang's metric Exercise 14: Advanced rules Exercise 15: Filtering with support and conviction Exercise 16: Using multi-metric filtering to cross-promote books

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.

Exercise 1: Aggregation Exercise 2: Performing aggregation Exercise 3: Defining an aggregation function Exercise 4: The Apriori algorithm Exercise 5: Pruning and Apriori Exercise 6: Identifying frequent itemsets with Apriori Exercise 7: Selecting a support threshold Exercise 8: Basic Apriori results pruning Exercise 9: Generating association rules Exercise 10: Pruning with lift Exercise 11: Pruning with confidence Exercise 12: Advanced Apriori results pruning Exercise 13: Aggregation and filtering Exercise 14: Applying Zhang's rule Exercise 15: Advanced filtering with multiple metrics

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.

Exercise 1: Heatmaps Exercise 2: Itemset-support visualiseren

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Exercise 3: Heatmaps met lift Exercise 4: Heatmaps interpreteren Exercise 5: Spreidingsdiagrammen Exercise 6: Prunen met scatterplots Exercise 7: Optimaliteit van de support-confidencegrens Exercise 8: Parallel coordinates-plot Exercise 9: Regels visualiseren met parallelle coördinaten Exercise 10: Een parallelle-coördinatenplot verfijnen Exercise 11: Gefeliciteerd!