Eseguire l'aggregazione

Dopo alcuni piccoli incarichi di consulenza per una biblioteca e un venditore di ebook, hai finalmente ricevuto il tuo primo grande progetto di Market Basket Analysis: consigliare un rivenditore online di regali originali sulle cross‑promotions. Poiché il rivenditore non ha mai assunto prima un data scientist, vorrebbe che iniziassi esplorando i suoi dati di transazione. Ti ha chiesto di eseguire l'aggregazione per tutti i signs nel dataset e di calcolare anche il supporto per questa categoria. Nota che pandas è già stato importato come pd. Inoltre, i dati sono stati importati in formato one‑hot encoded come onehot.

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Analisi del carrello in Python

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Istruzioni dell'esercizio

Seleziona il sottoinsieme delle colonne del DataFrame che contengono la stringa sign.
Stampa il supporto per signs.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Select the column headers for sign items
sign_headers = [i for i in onehot.columns if i.lower().find('sign')>=0]

# Select columns of sign items using sign_headers
sign_columns = onehot[____]

# Perform aggregation of sign items into sign category
signs = sign_columns.sum(axis = 1) >= 1.0

# Print support for signs
print('Share of Signs: %.2f' % ____.mean())

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Questo esercizio fa parte del corso

Analisi del carrello in Python

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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: Aggregazione Exercise 2: Eseguire l'aggregazione

Esercizio in corso

Exercise 3: Definire una funzione di aggregazione Exercise 4: L'algoritmo Apriori Exercise 5: Potatura e Apriori Exercise 6: Identificare i frequent itemsets con Apriori Exercise 7: Selezionare una soglia di supporto Exercise 8: Potatura base dei risultati di Apriori Exercise 9: Generare regole di associazione Exercise 10: Potatura con lift Exercise 11: Potatura con la confidence Exercise 12: Potatura avanzata dei risultati di Apriori Exercise 13: Aggregazione e filtraggio Exercise 14: Applicare la regola di Zhang Exercise 15: Filtri avanzati con metriche multiple

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!