Cosa c’è nel carrello?

A questo punto è importante riuscire a visualizzare cosa c’è nel carrello. I data frame sono comodi da usare, ma è più efficace avere un grafico per mostrare i diversi articoli presenti nei tuoi carrelli, o almeno una parte di essi.

L’oggetto transazionale Online_trx è caricato nello spazio di lavoro. Userai la funzione itemFrequencyPlot() del pacchetto arules per ispezionare visivamente i prodotti più acquistati in questo insieme di transazioni e trovare il grafico più adatto.

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Analisi del carrello in R

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# Display the five most popular items
itemFrequencyPlot(___, 
                  ___ = 5)

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Analisi del carrello in R

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What’s in your basket? In this first chapter, you’ll learn how market basket analysis (MBA) can be used to look into baskets and dig into itemsets to better understand customers and predict their needs. Using tidyverse and dplyr you’ll discover how many baskets can be created from a given set of items, and learn the power of using market basket analysis for online shopping, offline shopping, and use cases beyond retail.

Exercise 1: Market basket introduction Exercise 2: Baskets and items Exercise 3: Single basket Exercise 4: What's in the basket?Exercise 5: Item combinations Exercise 6: Number of possible baskets Exercise 7: Subsets and supersets Exercise 8: Plot number of possible baskets Exercise 9: What is market basket analysis ?Exercise 10: Two baskets Exercise 11: Multiple baskets Exercise 12: Looking at specific items

In this chapter, you’ll convert transactional datasets to a basket format, ready for analysis using the Apriori algorithm. You’ll then be introduced to the three main metrics for market basket analysis: support, confidence, and lift, before getting hands-on with the Apriori algorithm to extract rules from a transactional dataset. Lastly, You explore how the arules package is used for market basket analysis to retrieve basket rules and to help you find the most informative and relevant rules.

Exercise 1: Transactional data Exercise 2: Transactionalizing the online data frame Exercise 3: Inspector retail Exercise 4: Density of the item matrix?Exercise 5: Metrics in market basket analysis Exercise 6: The support measure Exercise 7: Confidence and lift measures Exercise 8: Changing the appearance of rules Exercise 9: The apriori algorithm Exercise 10: Apriori principle Exercise 11: Let's go shopping for rules Exercise 12: Parameters of the apriori Exercise 13: “If this then that” with the apriori Exercise 14: Playing with the appearance Exercise 15: Redundant rules Exercise 16: Interpretation of rules

Let’s get visual. In this chapter, you’ll visually inspect the set of rules you have previously extracted. Visualizations in market basket analysis are vital given that often you are dealing with large sets of extracted rules. You’ll use the arulesViz package to create barplots, scatterplots, and graphs to visualize your sets of inferred rules. You’ll then turn sets of plots into interactive plots, making it is easier to drill into the mined association rules.

Exercise 1: Vediamo cosa c’è nel carrello Exercise 2: Cosa c’è nel carrello?

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Exercise 3: Quali sono i più popolari?Exercise 4: Rendiamo le visualizzazioni più accattivanti Exercise 5: Visualizzare le metriche Exercise 6: Scatter plot Exercise 7: Grafici ArulesViz Exercise 8: Dai vita alle regole Exercise 9: Dalle regole alle visualizzazioni basate su grafi Exercise 10: Giochiamo con i grafi Exercise 11: Capire il grafico Exercise 12: Portabilità del tuo grafo Exercise 13: Grafici alternativi delle regole Exercise 14: Visualizzazioni basate su matrici di gruppi Exercise 15: Grafici a coordinate parallele Exercise 16: Padroneggiare il RuleExplorer

We’re going to the movies. In this final chapter, you’ll apply everything you’ve learned as you work with a movie dataset. Using market basket analysis you’ll turn this dataset into a movie recommendation system, using information from movie transactions to understand and predict what your audience might want to watch next.

Exercise 1: Recap on transactions Exercise 2: Gettting familiar with the MovieLens dataset Exercise 3: Movie transactions Exercise 4: Frequency plots Exercise 5: Mining association rules Exercise 6: Popularity of movies Exercise 7: Picking the right movie parameters Exercise 8: Extracting movie rules Exercise 9: Visualizing transactions and rules Exercise 10: Where is Pulp Fiction?Exercise 11: Visualizing movie rules Exercise 12: Our favorite movies as a graph Exercise 13: Making the most of market basket analysis Exercise 14: What influenced Pulp Fiction?Exercise 15: What did Pulp Fiction influence?Exercise 16: Use your market basket skills!