Machine learning models are highly dependent on the quality of the data they are trained on. Often, the datasets you encounter will require significant preparation before they can be effectively used by algorithms. Real-world data is frequently incomplete, inconsistent, or not in the correct format for processing.

This chapter introduces fundamental data preprocessing techniques. You will learn practical methods for:

• Identifying and handling missing values in your dataset.
• Applying feature scaling, such as Normalization $$x' = \frac{x - \min(x)}{\max(x) - \min(x)}$$ and Standardization $$x' = \frac{x - \mu}{\sigma}$$ to bring numerical features to a common scale.
• Encoding categorical (non-numeric) features into a numerical representation that models can understand, using methods like One-Hot Encoding.
• Implementing the split of data into training and testing sets.

By the end of this chapter, you will understand why these steps are necessary and how to perform basic data cleaning and transformation tasks to prepare data for machine learning models.