You are working for a consulting firm in their machine learning practice. Your current client is a sports equipment manufacturer. You are building a linear regression model to predict ski and snowboard sales based on the daily snowfall in various regions around the country.
After you have cleaned and performed feature engineering on your CSV data, which of the following tasks would you perform next?

Answer options:

A.Use the scikit-learn cross_validate method to evaluate the estimation precision of your model
B.Load your data into a pandas DataFrame and remove header rows and any superfluous features
C.Use one-hot encoding to convert categorical values, such as ‘region of the country’ to numerical values
D.Shuffle your data using a shuffling technique

Answer: D
Option A is incorrect. The scikit-learn cross_validate method is used to evaluate your model’s precision while tuning the model’s hyperparameters. (See Scikit-Learn user guide titled cross_validate)
Option B is incorrect. Using a Pandas DataFrame to remove superfluous rows and features is part of cleaning and doing feature engineering of your data, which you have already done.
Option C is incorrect. One-hot encoding is another way to do feature engineering on your data in preparation for training. You have already completed the cleaning and feature engineering of your data.
Option D is correct. For a linear regression model, once you have cleaned and engineered your data you need to shuffle the data to prevent overfitting and to reduce variance. (See Amazon Machine Learning developer guide titled The Amazon Machine Learning Process)
Reference:
Please see the Amazon Machine Learning developer guide titled Machine Learning Concepts, and the Amazon Machine Learning developer guide titled The Amazon Machine Learning Process