Filtrare con support e conviction

Nel video abbiamo parlato del lavoro di consulenza continuativo che stai svolgendo per il fondatore di una start-up che vende ebook. Il fondatore si è rivolto a te con il DataFrame rules, che contiene il lavoro di una data scientist che in precedenza faceva parte del team. Include le colonne per antecedents e consequents, insieme alle prestazioni di ciascuna di queste regole rispetto a diverse metriche.

Il tuo obiettivo è applicare un filtro multi-metrica al dataset per individuare regole potenzialmente utili. Nota che pandas è disponibile come pd e numpy come np. Inoltre, rules è già definito e disponibile.

Questo esercizio fa parte del corso

Analisi del carrello in Python

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Istruzioni dell'esercizio

Usa il metodo .head() con print per visualizzare in anteprima il dataset.
Seleziona il sottoinsieme di regole con un antecedent support maggiore di 0.05.
Seleziona il sottoinsieme di regole con un consequent support maggiore di 0.02.
Seleziona il sottoinsieme di regole con una conviction maggiore di 1.01.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Preview the rules DataFrame using the .head() method
print(____)

# Select the subset of rules with antecedent support greater than 0.05
rules = rules[rules['antecedent support'] > ____]

# Select the subset of rules with a consequent support greater than 0.02
rules = rules[____]

# Select the subset of rules with a conviction greater than 1.01
rules = ____

# Print remaining rules
print(rules)

Modifica ed esegui il codice

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: Confidenza e lift Exercise 2: Consigliare libri con il supporto Exercise 3: Raffinare il supporto con la confidence Exercise 4: Ulteriore affinamento con il lift Exercise 5: Leverage e conviction Exercise 6: Lift vs leverage Exercise 7: Calcolare la conviction Exercise 8: Calcolare la conviction con una funzione Exercise 9: Promuovere ebook con conviction Exercise 10: Associazione e dissociazione Exercise 11: Calcolare associazione e dissociazione Exercise 12: Definire la metrica di Zhang Exercise 13: Applicare la metrica di Zhang Exercise 14: Regole avanzate Exercise 15: Filtrare con support e conviction

Esercizio in corso

Exercise 16: Usare il filtraggio multi-metrica per la promozione incrociata dei libri

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!