Heatmap con lift

La fondatrice apprezza la heatmap che hai creato per il suo servizio di streaming. Dopo aver approfondito il progetto, però, decidete che è importante esaminare anche altre metriche prima di prendere una decisione finale su quali film concedere in licenza. In particolare, la fondatrice suggerisce di scegliere una metrica che indichi se i valori di support siano più alti di quanto ci aspetteremmo dato il support individuale dei singoli film.

Ricordi che il lift è adatto a questo scopo e decidi di usarlo come metrica. Ricordi anche che il lift ha una soglia importante a 1.0 e stabilisci che è utile sostituire la barra dei colori con annotazioni, così puoi capire se un valore è maggiore di 1.0. Nota che le regole dell'esercizio precedente sono disponibili come rules.

Questo esercizio fa parte del corso

Analisi del carrello in Python

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Istruzioni dell'esercizio

Importa seaborn con il suo alias standard.
Trasforma il DataFrame contenente le regole in una matrice usando la metrica lift.
Genera una heatmap con annotazioni attive e barra dei colori disattivata.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import seaborn under its standard alias
____

# Transform the DataFrame of rules into a matrix using the lift metric
pivot = rules.____(index = 'consequents', 
                   columns = 'antecedents', values= '____')

# Generate a heatmap with annotations on and the colorbar off
sns.heatmap(pivot, annot = ____, ____)
plt.yticks(rotation=0)
plt.xticks(rotation=90)
plt.show()

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Analisi del carrello in Python

IntermediárioNível de habilidade

4.9+

Inizia il corso gratis

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: 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: Heatmap Exercise 2: Visualizzazione del supporto degli itemset Exercise 3: Heatmap con lift

Esercizio in corso

Exercise 4: Interpretare le heatmap Exercise 5: Scatter plot Exercise 6: Potatura con scatter plot Exercise 7: Ottimalità del confine supporto-confidenza Exercise 8: Grafico a coordinate parallele Exercise 9: Usare le coordinate parallele per visualizzare le regole Exercise 10: Raffinare un grafico a coordinate parallele Exercise 11: Congratulazioni!