Finding problems with the Conversion Rate dataset

The Setting

The conversion rate dataset, to be found at "./data/conversion_data.csv" contains the following columns:

• country : user country based on the IP address
• age : user age. Self-reported at sign-in step
• new_user : whether the user created the account during this session or had already an account and simply came back to the site
• source : marketing channel source
  • Ads: came to the site by clicking on an advertisement
  • Seo: came to the site by clicking on search results
  • Direct: came to the site by directly typing the URL on the browser
• total_pages_visited: number of total pages visited during the session.
  • This is a proxy for time spent on site and engagement during the session.
• converted: this is our label. 1 means they converted within the session, 0 means they left without buying anything.

The eventual task for this dataset is to predict the converted column. But right now we'll focus on a few preparatory tasks.

The Task

Your task is to write a function called detect_outliers() that specializes detecting outliers in age variables!

1. Accepts the following parameters:
   • data: A dataframe (in this case, the conversion rate dataframe read from the csv file)
     • the dataframe should have a column called age
2. It should return
   • A list of values for the age column/variable which are deemed outliers
3. The function should raise an error if there is no column with the name age