1. Learn
    2. /
  2. Courses
    3. /
  3. Market Basket Analysis in R
  • 1

    Introduction to Market Basket Analysis

    Free

    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.

  • 2

    Metrics & Techniques in Market Basket Analysis

    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.

  • 3

    Visualization in Market Basket Analysis

    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.

  • 4

    Case Study: Market basket with Movies

    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.

Initializing

Exercise

Bringing rules to life

In the previous exercise, you learned a range of plots to visualize your association rules. However, all of them generated static images that make digging into each association rule to be cumbersome. This could easily be resolved by using the plotly engine while using the plot function, which generates an interactive HTML plot using the plotly visualization framework. By using interactive plotting, which rule has the highest lift and highest confidence in rules_online?

Instructions

50 XP

Possible answers