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  3. Fraud Detection in Python

Exercise

Using list of terms

Oftentimes you don't want to search on just one term. You probably can create a full "fraud dictionary" of terms that could potentially flag fraudulent clients and/or transactions. Fraud analysts often will have an idea what should be in such a dictionary. In this exercise you're going to flag a multitude of terms, and in the next exercise you'll create a new flag variable out of it. The 'flag' can be used either directly in a machine learning model as a feature, or as an additional filter on top of your machine learning model results. Let's first use a list of terms to filter our data on. The dataframe containing the cleaned emails is again available as df.

Instructions

100 XP
  • Create a list to search for including 'enron stock', 'sell stock', 'stock bonus', and 'sell enron stock'.
  • Join the string terms in the search conditions.
  • Filter data using the emails that match with the list defined under searchfor.