Raw data is rarely suitable for direct input into machine learning algorithms. Models require clean, properly formatted numerical data to function effectively. This chapter focuses on the essential techniques for transforming raw datasets into formats optimized for model training.

You will learn about the standard steps in a typical machine learning project, with a specific focus on the data preparation phase. We will cover practical methods for:

• Feature Engineering: Creating meaningful input variables from existing data.
• Handling Categorical Data: Converting non-numerical features (like text labels) into numerical representations using techniques such as one-hot encoding.
• Feature Scaling: Normalizing or standardizing numerical features to ensure they are on a comparable scale, which is often required by algorithms.
• Data Splitting: Dividing your dataset correctly into training and testing sets to evaluate model performance accurately.
• Using Scikit-learn Pipelines: Streamlining these preprocessing steps into a consistent and reusable workflow.

By the end of this chapter, you will be able to apply these preprocessing techniques using Python libraries like Pandas and Scikit-learn to prepare data effectively for machine learning tasks.