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Ejercicio

One-hot encoding transaction data

Throughout the course, we will use a common pipeline for preprocessing data for use in market basket analysis. The first step is to import a pandas DataFrame and select the column that contains transactions. Each transaction in the column will be a string that consists of a number of items, each separated by a comma. The next step is to use a lambda function to split each transaction string into a list, thereby transforming the column into a list of lists.

In this exercise, you'll start with the list of lists from the grocery dataset, which is available to you as transactions. You will then transform transactions into a one-hot encoded DataFrame, where each column consists of TRUE and FALSE values that indicate whether an item was included in a transaction.

Instrucciones

100 XP
  • From the mlxtend.preprocessing, import TransactionEncoder
  • Instantiate a transaction encoder and identify the unique items in transactions.
  • One-hot encode transactions in an array and assign its values to onehot.
  • Convert the array into a pandas DataFrame using the item names as column headers.