MLS-C01 Question 101
Single answerA data scientist is building a machine learning model to predict customer churn for a subscription-based service. The dataset includes a 'Join_Date' column, which represents the date a customer joined the service. However, the 'Join_Date' column is in its raw date format. What is the most appropriate feature engineering technique to apply to this column to improve model performance?
- A
Convert the 'Join_Date' into a numerical feature representing the number of days since the customer joined.
- B
One-hot encode the 'Join_Date' column to represent each unique date as a separate feature.
- C
Apply tokenization to the 'Join_Date' column to split it into individual components (year, month, day).
- D
Remove the 'Join_Date' column as it is not directly useful for prediction.
Show answer and explanation
Correct answer: A
Explanation
To effectively use the 'Join_Date' column, it should be transformed into a numerical feature that reflects the temporal relationship between the customer joining and their churn behavior. This transformation ensures that the data is both meaningful and usable by the machine learning model, while avoiding issues like sparsity or loss of information.
- A. Correct.
Converting the 'Join_Date' into a numerical feature representing the number of days since the customer joined is appropriate because it captures a meaningful temporal relationship between the join date and customer churn, which can improve the model's predictive power.
- B. Incorrect.
One-hot encoding the 'Join_Date' column would result in too many features if the dataset spans many unique dates, making it impractical and leading to a sparsity issue.
- C. Incorrect.
Applying tokenization to the 'Join_Date' column is not suitable as it is generally used for text data and does not capture the temporal relationship inherent in a date.
- D. Incorrect.
Removing the 'Join_Date' column discards potentially valuable information that could help the model understand temporal patterns related to customer churn.