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.
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.
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.
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.
Kuna nini katika kikapu chako? Katika sura hii ya kwanza, utajifunza jinsi uchambuzi wa kikapu cha soko (MBA) unaweza kutumika kuangalia katika vikapu na kuchimba katika seti za vitu ili kuelewa vizuri wateja na kutabiri mahitaji yao. Kutumia tidyverse na dplyr utagundua ni vikapu vingapi vinaweza kuundwa kutoka kwa seti fulani ya vitu, na kujifunza nguvu ya kutumia uchambuzi wa kikapu cha soko kwa ununuzi wa mtandaoni, ununuzi wa nje ya mtandao, na matumizi ya kesi zaidi ya rejareja. Katika sura hii, wewe <unk>ll kubadilisha shughuli datasets kwa kikapu format, tayari kwa ajili ya uchambuzi kwa kutumia Apriori algorithm. Kisha utaanzishwa kwa vipimo vitatu kuu kwa ajili ya uchambuzi wa soko kikapu: msaada, imani, na kuinua, kabla ya kupata mikono juu na Apriori algorithm kuchimba sheria kutoka kwa seti ya data ya shughuli. Mwishowe, utagundua jinsi kifurushi cha arules kinavyotumiwa kwa uchambuzi wa kikapu cha soko kupata sheria za kikapu na kukusaidia kupata sheria zenye habari zaidi na zinazohusiana. Hebu kupata visual. Katika sura hii, wewe <unk>ll kuona kukagua seti ya sheria umefanya hapo awali extracted. Visualizations katika uchambuzi soko kikapu ni muhimu kutokana na kwamba mara nyingi wewe ni kushughulika na seti kubwa ya sheria extracted. Wewe itabidi kutumia arulesViz mfuko kuunda barplots, scatterplots, na grafu ya kuona seti yako ya sheria inferred. Kisha wewe <unk>ll kugeuka seti ya njama katika njama maingiliano, kufanya hivyo ni rahisi kuchimba ndani ya sheria ya ushirika kuchimbwa. Sisi ni kwenda sinema. Katika sura hii ya mwisho, wewe itabidi kutumia kila kitu umejifunza kama wewe kazi na seti ya data ya filamu. Kutumia uchambuzi wa kikapu cha soko utageuza seti hii ya data kuwa mfumo wa kupendekeza filamu, ukitumia habari kutoka kwa shughuli za filamu kuelewa na kutabiri kile watazamaji wako wanaweza kutaka kutazama baadaye.