Doğru film parametrelerini seçmek

Hâlâ apriori() fonksiyonuna hangi parametreleri ekleyeceğine nasıl karar vereceğini merak ediyorsun. Özellikle, minimum destek (support), güven (confidence) ve gerekirse lift ölçüleri için hangi eşik değerlerini kullanacağına karar vermen gerekiyor. apriori() fonksiyonuna eklediğin parametreler elde edeceğimiz film kuralları kümesini etkileyecek. Hangi parametrelerin en uygun olduğuna karar vermene yardımcı olmak için, kural sayısının grafiğini oluşturalım. Bu egzersizde, apriori() fonksiyonunda kullanılacak en uygun parametre kombinasyonuna karar vermek için bir grafik oluşturacaksın.

Bu egzersiz

R ile Pazar Sepeti Analizi

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Set of confidence levels
confidenceLevels = seq(from=0.95, to=0.5, by=-0.05)

# Create empty vector
rules_sup04 = NULL

# Apriori algorithm with a support level of 40%
for (i in 1:length(___)) {
  rules_sup04[i] = 
  ___(apriori(movie_trx,
                 parameter=list(sup=___, 
                                conf=confidenceLevels[i],
                                target="___")))
}

Kodu Düzenle ve Çalıştır

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: 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: İşlemlere kısa bir özet Exercise 2: MovieLens veri kümesiyle tanışma Exercise 3: Film işlemleri Exercise 4: Frekans grafikleri Exercise 5: İlişkilendirme kurallarını çıkarma Exercise 6: Filmlerin popülerliği Exercise 7: Doğru film parametrelerini seçmek

Geçerli Egzersiz

Exercise 8: Film kurallarının çıkarılması Exercise 9: İşlemleri ve kuralları görselleştirme Exercise 10: Pulp Fiction nerede?Exercise 11: Film kurallarını görselleştirme Exercise 12: Favori filmlerimizi bir grafik olarak Exercise 13: Market basket analizinden en iyi şekilde yararlanmak Exercise 14: Pulp Fiction’ı ne etkiledi?Exercise 15: Pulp Fiction neyi etkiledi?Exercise 16: Market basket becerilerini kullan!