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Question 1 of 29
A data scientist is tasked with building a predictive model to forecast sales for a retail company based on historical sales data, promotional activities, and seasonal trends. The data scientist decides to use a linear regression model for this purpose. After training the model, they find that the model\'s performance is suboptimal, with a high mean squared error (MSE) of 2500. To improve the model, they consider adding polynomial features to capture non-linear relationships in the data. If the original feature set consists of two features, \\( x_1 \\) (previous sales) and \\( x_2 \\) (promotional spend), what would be the new feature set after adding polynomial features up to the second degree?
\( \{1, x_1, x_2, x_1^2, x_2^2, x_1x_2\} \)
\( \{1, x_1, x_2, x_1^2, x_2^2\} \)
\( \{1, x_1, x_2, x_1^2\} \)
\( \{1, x_1, x_2, x_1x_2\} \)

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