Summary

In this comprehensive chapter, we covered essential concepts in pre-modeling data for analytics and feature engineering. Mastering these techniques is vital for data scientists to effectively handle real-world datasets and build accurate machine learning models.

Understanding techniques such as data min-max scaling, z-score scaling, and feature engineering can enhance model performance; transformations such as logarithmic, Box-Cox, and exponential help reshape data for better algorithm compatibility; dimensionality reduction methods such as PCA and t-SNE simplify and visualize data and aid in effective model building; and handling imbalanced data with resampling and ensemble techniques ensure balanced datasets and unbiased predictions.

Additionally, we covered feature engineering techniques, including one-hot encoding, label encoding, and target encoding. These techniques allow us to craft new and informative representations of data. Feature engineering involves selecting...