Preparare i dati per la market basket analysis

In tutto il corso, incontrerai i dati in uno dei due formati: un DataFrame pandas o una lista di liste. Gli oggetti DataFrame verranno creati importando un file csv con pandas. Saranno composti da una singola colonna di dati, in cui ogni elemento contiene una stringa di articoli presenti in una transazione, separati da virgole, come nella tabella seguente.

In questo esercizio, farai pratica a caricare i dati da un file csv e a prepararli per l'uso come lista di liste. Nota che il percorso del dataset del negozio di alimentari è già definito ed è disponibile come groceries_path.

Transaction
'milk,bread,biscuit'
'bread,milk,biscuit,cereal'
…
'tea,milk,coffee,cereal'

Questo esercizio fa parte del corso

Analisi del carrello in Python

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Istruzioni dell'esercizio

Importa il pacchetto pandas con l'alias pd.
Usa pandas per leggere il file csv al percorso specificato da groceries_path.
Seleziona la colonna Transaction dal DataFrame e dividi ogni stringa di articoli separati da virgole in una lista.
Converte il DataFrame di transazioni in una lista di liste.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import pandas under the alias pd
import ____ as pd

# Load transactions from pandas
groceries = pd.____(groceries_path)

# Split transaction strings into lists
transactions = groceries['____'].apply(lambda t: t.split(','))

# Convert DataFrame column into list of strings
transactions = list(____)

# Print the list of transactions
print(transactions)

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: Che cos’è la market basket analysis?Exercise 2: Le basi della market basket analysis Exercise 3: Cross-selling dei prodotti Exercise 4: Identificare le regole di associazione Exercise 5: Antecedenti e conseguenti multipli Exercise 6: Preparare i dati per la market basket analysis

Esercizio in corso

Exercise 7: Generare regole di associazione Exercise 8: La metrica più semplice Exercise 9: One-hot encoding dei dati di transazione Exercise 10: Calcolare la metrica di supporto

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: 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!