Inspector retail

Pada latihan sebelumnya, Anda telah melihat sekilas data dengan merangkum himpunan data transaksional Anda. Pada latihan ini, Anda akan menggali lebih dalam menggunakan fungsi inspect(). Penting untuk dapat memeriksa transaksi dan membatasi jumlah transaksi yang diperiksa jika jumlahnya terlalu besar. Objek transaksional Online_trx sudah dimuat di ruang kerja Anda. Sekarang giliran Anda bereksperimen dengan inspeksi transaksi. Perlu diingat, memeriksa jutaan transaksi tanpa batasan dapat menyebabkan sesi R menjadi tidak responsif (hang).

Latihan ini adalah bagian dari kursus

Analisis Keranjang Belanja di R

Lihat Kursus

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# inspect first 3 transactions
___(___(Online_trx,3))

# inspect last 5 transactions
___(___(Online_trx,___))

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Analisis Keranjang Belanja di R

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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: Data transaksional Exercise 2: Mentransaksikan data frame online Exercise 3: Inspector retail

Latihan Saat Ini

Exercise 4: Kepadatan item matrix?Exercise 5: Metrik dalam analisis keranjang pasar Exercise 6: Ukuran support Exercise 7: Ukuran confidence dan lift Exercise 8: Mengubah tampilan aturan Exercise 9: Algoritme Apriori Exercise 10: Prinsip Apriori Exercise 11: Ayo berbelanja aturan Exercise 12: Parameter pada apriori Exercise 13: “Jika ini maka itu” dengan apriori Exercise 14: Bermain dengan parameter appearance Exercise 15: Aturan redundan Exercise 16: Interpretasi aturan

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!