Cestino singolo

Nei prossimi esercizi lavorerai con un insieme di dati che contiene le transazioni di un’azienda di e-commerce con sede nel Regno Unito. Queste transazioni coprono tutto il primo trimestre del 2011.

In particolare, esaminerai più da vicino un singolo cestino da questo dataset. Tocca a te calcolare la dimensione totale del cestino e il numero di prodotti distinti.

Il dataset chiamato Online_Retail_2011_Q1 è disponibile nel tuo workspace. Alcune colonne importanti del dataset sono:

StockCode: numero univoco assegnato a ciascun prodotto distinto;
InvoiceNo: numero univoco assegnato a ciascuna transazione;
Description: descrizione del prodotto;
Quantity: quantità acquistata per transazione.

Questo esercizio fa parte del corso

Analisi del carrello in R

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Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Have a glimpse at the dataset
glimpse(Online_Retail_2011_Q1)

# Filter a single basket
One_basket = Online_Retail_2011_Q1 %>%
				___(___ == 540180)

___(___)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

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: Introduzione alla market basket analysis Exercise 2: Basket e item Exercise 3: Cestino singolo

Esercizio in corso

Exercise 4: Cosa c’è nel carrello?Exercise 5: Combinazioni di articoli Exercise 6: Numero di carrelli possibili Exercise 7: Sottoinsiemi e soprainsiemi Exercise 8: Grafica il numero di carrelli possibili Exercise 9: Che cos’è la market basket analysis?Exercise 10: Due carrelli Exercise 11: Più carrelli Exercise 12: Osservare articoli specifici

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: Let's see what's in the basket Exercise 2: What's in the basket?Exercise 3: What's most popular ?Exercise 4: Getting fancy with the visuals Exercise 5: Visualizing metrics Exercise 6: Scatterplots Exercise 7: ArulesViz plots Exercise 8: Bringing rules to life Exercise 9: From rules to graph based visualizations Exercise 10: Playing with graphs Exercise 11: Understanding the graph Exercise 12: Portability of your graph Exercise 13: Alternative rule plots Exercise 14: Group matrix based visualizations Exercise 15: Parallel coordinates plots Exercise 16: Mastering the 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!