Data Preprocessing with Scikit-Learn

Data preprocessing is a crucial step in the machine learning pipeline, ensuring that raw data is cleaned, transformed, and prepared for modeling. Without proper preprocessing, even the most advanced models can produce inaccurate results. In this chapter, we explore the essential techniques used to effectively preprocess data in Scikit-Learn.

You'll start by understanding the importance of handling missing values and explore methods to fill these gaps. Next, you'll learn how to convert categorical data into numerical formats using techniques such as one-hot encoding and label encoding, which are essential for algorithms that require numerical input. Scaling and normalization will also be covered to help you prepare your features by ensuring they are on a similar scale, a vital step when dealing with algorithms sensitive to feature magnitude.

Additionally, you'll gain insights into feature selection and extraction, focusing on how to reduce dimensionality and improve model performance by selecting the most relevant features. By the end of this chapter, you will be equipped with a robust set of preprocessing tools, enabling you to transform raw data into a format that enhances the predictive power of your models.