Filtri avanzati con metriche multiple

In precedenza abbiamo usato i dati di un negozio online di regali originali per trovare antecedenti utili a promuovere un conseguente mirato. Poiché l’insieme delle regole potenziali era ampio, abbiamo fatto affidamento sull’algoritmo Apriori e su filtri con più metriche per restringere il campo. In questo esercizio, esamineremo l’intero insieme di regole e ne individueremo una utile, invece di puntare a un antecedente specifico.

Nota che i dati sono stati caricati, preprocessati e trasformati in one-hot, e sono disponibili come onehot. Inoltre, apriori() e association_rules() sono stati importati da mlxtend. In questo esercizio applicherai l’algoritmo Apriori per identificare gli insiemi frequenti. Recupererai poi l’insieme delle regole di associazione a partire dagli itemset e applicherai un filtro con metriche multiple.

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Analisi del carrello in Python

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Istruzioni dell'esercizio

Applica l’algoritmo Apriori agli itemset one-hot encoded con una soglia minima di supporto pari a 0.001.
Estrai le regole di associazione usando una soglia minima di supporto pari a 0.001.
Imposta antecedent_support a 0.002 e consequent_support a 0.01.
Imposta confidence maggiore di 0.60 e lift maggiore di 2.50.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Apply the Apriori algorithm with a minimum support threshold of 0.001
frequent_itemsets = ____(onehot, min_support = ____, use_colnames = True)

# Recover association rules using a minium support threshold of 0.001
rules = ____(frequent_itemsets, metric = '____', min_threshold = 0.001)

# Apply a 0.002 antecedent support threshold, 0.60 confidence threshold, and 2.50 lift threshold
filtered_rules = rules[(rules['antecedent support'] > ____) &
						(____['consequent support'] > 0.01) &
						(rules['____'] > ____) &
						(____ > 2.50)]

# Print remaining rule
print(filtered_rules[['antecedents','consequents']])

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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 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

Esercizio in corso

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!