Machine learning models rarely work well with raw, unprocessed data. Real-world datasets often contain inconsistencies, missing values, and features measured on vastly different scales or in non-numerical formats. Many algorithms expect data to be clean, numerical, and properly scaled for optimal performance. For instance, algorithms calculating distances between points, like K-Nearest Neighbors, or those using gradient descent optimization, like linear regression with regularization, are sensitive to the scale of input features. Similarly, most algorithms require numerical input, making it necessary to convert categorical text data into a suitable format.

This chapter focuses on essential data preparation techniques using Scikit-learn's tools. You will learn how to:

• Scale numerical features using methods like standardization ($(X - \mu) / \sigma$) and normalization (scaling to a $[0, 1]$ range) to ensure features contribute appropriately to model training.
• Encode categorical features into numerical representations using strategies such as One-Hot Encoding and Ordinal Encoding.
• Handle missing values through imputation strategies, replacing missing entries with estimated or statistical values.

Mastering these preprocessing steps is fundamental to building effective machine learning models. We will explore Scikit-learn's transformer API for implementing these techniques efficiently.