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What are we predicting?

Which of these fields (or columns) is the value we are trying to predict for?

  • TAXES
  • SALESCLOSEPRICE
  • DAYSONMARKET
  • LISTPRICE

Cet exercice fait partie du cours

<cours>Feature Engineering with PySpark</cours>
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Instructions de l’exercice

  • From the listed columns above, identify which one we will use as our dependent variable $Y$.
  • Using the loaded data set df, filter it down to our dependent variable with select(). Store this dataframe in the variable Y_df.
  • Display summary statistics for the dependent variable using describe() on Y_df and calling show() to display it.

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

# Select our dependent variable
Y_df = df.____([____])

# Display summary statistics
Y_df.____().____()
Modifier et exécuter le code