Linear Regression Model for Ski and Snowboard Sales Prediction

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.

After cleaning and feature engineering the CSV data, the next step for building a linear regression model to predict ski and snowboard sales based on daily snowfall in various regions around the country is to perform data preparation.

Option A: Use the scikit-learn cross_validate method to evaluate the estimation precision of your model This option is related to model evaluation, which is done after data preparation. Cross-validation is a method used to estimate the performance of a model on an independent dataset. Cross-validation involves partitioning the dataset into k equally sized parts, training the model on k-1 of the parts, and evaluating the performance of the model on the remaining part. Although it is important to evaluate the model's performance, it is not the next step after data cleaning and feature engineering.

Option B: Load your data into a pandas DataFrame and remove header rows and any superfluous features This option is related to data cleaning and preparation. Loading the data into a pandas DataFrame enables data manipulation and feature engineering. Removing header rows and superfluous features such as irrelevant columns reduces the noise in the data and helps the model focus on the relevant features. This is the next step after cleaning and feature engineering.

Option C: Use one-hot encoding to convert categorical values, such as ‘region of the country' to numerical values This option is related to feature engineering, which is done after data cleaning. Categorical variables such as 'region of the country' cannot be used directly in a linear regression model because they are not numerical. One-hot encoding is a technique used to convert categorical variables to numerical variables by creating binary variables for each category. This is done to ensure that the model can understand the relationship between the categorical variable and the target variable. Although it is an important step, it is not the next step after data cleaning and feature engineering.

Option D: Shuffle your data using a shuffling technique. This option is related to data preparation, which is done after cleaning and feature engineering. Shuffling the data helps to eliminate any systematic patterns that may exist in the data. This is done to ensure that the model is not biased towards any particular subset of the data. Although it is important to shuffle the data, it is not the next step after data cleaning and feature engineering.

Therefore, the correct answer is B: Load your data into a pandas DataFrame and remove header rows and any superfluous features.