Visualizing itemset support
A content-streaming start-up has approached you for consulting services. To keep licensing fees low, they want to assemble a narrow library of movies that all appeal to the same audience. While they'll provide a smaller selection of content than the big players in the industry, they'll also be able to offer a low subscription fee.
You decide to use the MovieLens data and a heatmap for this project. Using a simple support-based heatmap will allow you to identify individual titles that have high support with other titles. The one-hot encoded data is available as the DataFrame onehot
. Additionally, pandas
is available as pd
, seaborn
is available as sns
, and apriori()
and association_rules()
have both been imported.
This exercise is part of the course
Market Basket Analysis in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Compute frequent itemsets using a minimum support of 0.07
frequent_itemsets = apriori(onehot, min_support = ____,
use_colnames = True, max_len = 2)
# Compute the association rules
rules = association_rules(____, metric = 'support',
min_threshold = 0.0)