Conclusion:

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• Based on the comparison of Mean Squared Error (MSE) between Linear Regression and Gradient Boosting, we can draw the following conclusions:

• Both Linear Regression and Gradient Boosting models perform very well in terms of MSE.

• The MSE value for the Linear Regression model is extremely low (approximately 2.308e-26), indicating a very accurate fit to the data.

• The Gradient Boosting model also achieves a low MSE value (approximately 1.691), indicating good performance in predicting the target variable.

• The close proximity of the MSE values suggests that both models are capable of capturing the underlying patterns in the data and making accurate predictions.