Aan de slagGa gratis aan de slag

Querying the recommendations

In the previous exercises, you've learned how to calculate a table with course recommendations on a daily basis. Now that this recommendations table is in the data warehouse, you could also quickly join it with other tables in order to produce important features for DataCamp students such as customized marketing emails, intelligent recommendations for students and other features.

In this exercise, you will get a taste of how the newly created recommendations table could be utilized by creating a function recommendations_for_user() which automatically gets the top recommended courses based per user ID for a particular rating threshold.

Deze oefening maakt deel uit van de cursus

Introduction to Data Engineering

Cursus bekijken

Oefeninstructies

  • Complete the query in the recommendations_for_user() function definition. It should join the courses table.
  • Complete the read_sql() function in recommendations_for_user(). The params argument is incomplete: it's missing a threshold.
  • Run the recommendations_for_user() function you defined in the last statements and observe the results.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

def recommendations_for_user(user_id, threshold=4.5):
    # Join with the courses table
    query = """
    SELECT title, rating FROM recommendations
    INNER JOIN ____ ON courses.course_id = recommendations.course_id
    WHERE user_id=%(user_id)s AND rating>%(threshold)s
    ORDER BY rating DESC
    """
    # Add the threshold parameter
    predictions_df = pd.read_sql(query, db_engine, params = {"user_id": user_id, 
                                                             "____": ____})
    return predictions_df.title.values

# Try the function you created
print(recommendations_for_user(12, 4.65))
Code bewerken en uitvoeren