Question: a) Why is data preparation important in predictive modeling? Explain with examples. b) Describe the significance of understanding how algorithms interpret the data in data

a) Why is data preparation important in predictive modeling? Explain with examples.

b) Describe the significance of understanding how algorithms interpret the data in data preparation.

c) Explain why correcting for skewed distributions may be necessary for linear regression but not for decision trees.

d) Discuss the iterative nature of feature creation in data preparation. Why is it important to iteratively create and test different features?

e) Overfitting is a common problem in predictive modeling. Explain what overfitting is and why it is a concern when deploying predictive models. How can a well-constructed sampling strategy help address overfitting?

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