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  3. Market Basket Analysis in Python

Exercise

Computing the support metric

In the previous exercise, you one-hot encoded a small grocery store's transactions as the DataFrame onehot. In this exercise, you'll make use of that DataFrame and the support metric to help the store's owner. First, she has asked you to identify frequently purchased items, which you'll do by computing support at the item-level. And second, she asked you to check whether the rule {jam} \(\rightarrow\) {bread} has a support of over 0.05. Note that onehot has been defined and is available. Additionally, pandas has been imported under the alias pd and numpy has been imported under the alias np.

Instructions 1/2

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  • 1
    • Compute the support value for each item in the one-hot encoded dataset, onehot.
    • Print the support values.
  • 2
    • Add a column, jam+bread, to onehot that is TRUE if both jam and bread are both in the transaction.
    • Print support.