Summary
We have covered a wide range of feature engineering techniques in this chapter. We used tools to drop redundant or highly correlated features. We explored the most common kinds of encoding—one-hot, ordinal, and hashing encoding. We then used transformations to improve the distribution of our features. Finally, we used common binning and scaling approaches to address skew, kurtosis, and outliers, and to adjust for features with widely different ranges. In the next chapter, we’ll learn how to fix messy data when aggregating.
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