Mengekstrak aturan film

Sekarang setelah Anda memiliki gambaran yang lebih jelas tentang ambang yang akan digunakan dalam fungsi apriori(), Anda siap mengekstrak aturan asosiasi dari himpunan data transaksional movie_subset. Mengekstrak kumpulan aturan film akan memungkinkan kita memberikan rekomendasi kepada pengguna terkait film yang ingin ditonton. Misalnya, setelah Anda menonton sejumlah film, film apa yang bisa Anda tonton berikutnya? Untuk menjawabnya, kita dapat memanfaatkan kumpulan film yang ditonton oleh semua pengguna lain dan menggunakan Market Basket Analysis untuk tujuan ini. Selain itu, beberapa aturan film dapat bersifat redundan, oleh karena itu Anda sebaiknya mengekstrak kumpulan aturan film yang tidak redundan agar mendapatkan informasi dan wawasan berharga tentang film yang ditonton pengguna.

Latihan ini adalah bagian dari kursus

Analisis Keranjang Belanja di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Extract rules with the apriori
rules_movies = apriori(movie_trx,
                          parameter = list(___ = ___,
                                           conf = ___,
                                           minlen = 2, 
                                           target = "___"))

# Summary of extracted rules
___(___)

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Latihan ini adalah bagian dari kursus

Analisis Keranjang Belanja di R

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

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: Rekap transaksi Exercise 2: Mengenal himpunan data MovieLens Exercise 3: Transaksi film Exercise 4: Plot frekuensi Exercise 5: Menambang aturan asosiasi Exercise 6: Popularitas film Exercise 7: Memilih parameter film yang tepat Exercise 8: Mengekstrak aturan film

Latihan Saat Ini

Exercise 9: Memvisualisasikan transaksi dan aturan Exercise 10: Di mana Pulp Fiction?Exercise 11: Memvisualisasikan aturan film Exercise 12: Film favorit kita sebagai sebuah graf Exercise 13: Memaksimalkan analisis keranjang pasar Exercise 14: Apa yang memengaruhi Pulp Fiction?Exercise 15: Apa yang dipengaruhi oleh Pulp Fiction?Exercise 16: Gunakan keterampilan market basket Anda!