Boeken aanbevelen met support

Een bibliotheek wil leden meer laten lezen en besluit market-basketanalyse te gebruiken om uit te zoeken hoe. Ze vragen jou om de analyse te doen en willen dat je de vijf hoogst beoordeelde boeken uit de goodbooks-10k-gegevensset gebruikt, die in de video werd geïntroduceerd. Je krijgt de gegevens in one-hot-gecodeerd formaat in een pandas DataFrame met de naam books.

Elke kolom in de DataFrame komt overeen met een boek en heeft de waarde TRUE als het boek in iemands bibliotheek staat en hoog is beoordeeld. Om het eenvoudiger te maken, werken we met verkorte boektitels: Hunger, Potter en Twilight.

Deze oefening maakt deel uit van de cursus

Market Basket Analysis in Python

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Oefeninstructies

Bereken de support voor {Hunger, Potter}.
Bereken de support voor {Hunger, Twilight}.
Bereken de support voor {Potter, Twilight}.

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# Compute support for Hunger and Potter
supportHP = np.logical_and(books['Hunger'], books['____']).mean()

# Compute support for Hunger and Twilight
supportHT = ____(books['Hunger'], books['Twilight']).mean()

# Compute support for Potter and Twilight
supportPT = np.logical_and(books['Potter'], books['Twilight']).____

# Print support values
print("Hunger Games and Harry Potter: %.2f" % supportHP)
print("Hunger Games and Twilight: %.2f" % supportHT)
print("Harry Potter and Twilight: %.2f" % supportPT)

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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 en lift Exercise 2: Boeken aanbevelen met support

Huidige oefening

Exercise 3: Support verfijnen met confidence Exercise 4: Verder verfijnen met lift Exercise 5: Leverage en conviction Exercise 6: Lift versus leverage Exercise 7: Conviction berekenen Exercise 8: Conviction berekenen met een functie Exercise 9: Ebooks promoten met conviction Exercise 10: Associatie en dissociatie Exercise 11: Associatie en dissociatie berekenen Exercise 12: Zhang's metriek definiëren Exercise 13: Zhang's metric toepassen Exercise 14: Geavanceerde regels Exercise 15: Filteren met support en conviction Exercise 16: Meerdere metrieken gebruiken om boeken kruislinks te promoten

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: Visualizing itemset support Exercise 3: Heatmaps with lift Exercise 4: Interpreting heatmaps Exercise 5: Scatterplots Exercise 6: Pruning with scatterplots Exercise 7: Optimality of the support-confidence border Exercise 8: Parallel coordinates plot Exercise 9: Using parallel coordinates to visualize rules Exercise 10: Refining a parallel coordinates plot Exercise 11: Congratulations!