Lift versus leverage

Como vimos no vídeo, lift e leverage são bem parecidos. Um é uma razão e o outro é uma diferença. Um tem limiar 0 para regras boas e o outro tem limiar 1. Você lembra qual é qual?

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Análise de Cesta de Compras em Python

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Análise de Cesta de Compras em 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: Confiança e lift Exercise 2: Recomendando livros com suporte Exercise 3: Refinando o suporte com confiança Exercise 4: Refinando ainda mais com lift Exercise 5: Leverage e conviction Exercise 6: Lift versus leverage

Exercicio Atual

Exercise 7: Calculando convicção Exercise 8: Calculando conviction com uma função Exercise 9: Promovendo e-books com conviction Exercise 10: Associação e dissociação Exercise 11: Calculando associação e dissociação Exercise 12: Definindo a métrica de Zhang Exercise 13: Aplicando a métrica de Zhang Exercise 14: Regras avançadas Exercise 15: Filtrando por support e conviction Exercise 16: Usando filtragem com múltiplas métricas para fazer cross-promoção de livros

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!