Kuralları canlandırmak

Önceki egzersizde, birliktelik kurallarını görselleştirmek için çeşitli grafikler öğrendin. Ancak bunların hepsi, her bir kuralın detayına inmeyi zahmetli hale getiren statik görseller üretiyordu. Bunu, plot fonksiyonunu kullanırken plotly altyapısını seçerek kolayca çözebilirsin; bu, plotly görselleştirme çatısını kullanarak etkileşimli bir HTML grafiği üretir. Etkileşimli görselleştirmeyi kullanarak, rules_online içindeki en yüksek lift ve en yüksek confidence değerine sahip kural hangisi?

Bu egzersiz

R ile Pazar Sepeti Analizi

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

İnteraktif egzersizlerimizden biriyle teoriyi pratiğe dökün

Egzersizi başlat

Bu egzersiz

R ile Pazar Sepeti Analizi

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Sepette neler var, bir bakalım Exercise 2: Sepette neler var?Exercise 3: En popüler hangisi?Exercise 4: Görselleri bir seviye yükseltmek Exercise 5: Metrikleri görselleştirme Exercise 6: Saçılım grafikleri Exercise 7: ArulesViz grafikleri Exercise 8: Kuralları canlandırmak

Geçerli Egzersiz

Exercise 9: Kurallardan grafik tabanlı görselleştirmelere Exercise 10: Grafiklerle oyna Exercise 11: Grafiği anlamak Exercise 12: Grafiğinin taşınabilirliği Exercise 13: Alternatif kural grafikleri Exercise 14: Grup matris tabanlı görselleştirmeler Exercise 15: Paralel koordinat grafikleri Exercise 16: RuleExplorer’ı Ustalaştırma

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!