Parametri di apriori

L'algoritmo apriori() richiede alcuni argomenti in input, come la soglia minima di supporto e il livello di confidenza. È importante capire quanto l'insieme delle regole estratte sia sensibile a questi parametri. In questo esercizio userai le transazioni del dataset Online retail Online_trx e applicherai l'algoritmo apriori() con valori diversi per i parametri di input.

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Analisi del carrello in R

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# Apply the apriori function to the Online retail dataset
rules_online = apriori(Online_trx,
                    ___ = list(___ = 0.01, 
                               ___ = ___, 
                               minlen = 2))

# Inspect the first rules
inspect(head(___))

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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: 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: Dati transazionali Exercise 2: Trasformare in transazioni il data frame online Exercise 3: Ispettore retail Exercise 4: Densità della matrice degli item?Exercise 5: Metriche nell'analisi delle market basket Exercise 6: La misura di supporto Exercise 7: Misure di confidence e lift Exercise 8: Modificare l'aspetto delle regole Exercise 9: L'algoritmo Apriori Exercise 10: Principio di Apriori Exercise 11: Andiamo a caccia di regole Exercise 12: Parametri di apriori

Esercizio in corso

Exercise 13: "Se questo allora quello" con apriori Exercise 14: Giocare con l'argomento appearance Exercise 15: Regole ridondanti Exercise 16: Interpretazione delle regole

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!