Part IV – Data Engineering & Preprocessing¶
"Data preparation accounts for about 80% of the work in machine learning projects."
From Raw Data to Model-Ready Features¶
Machine learning algorithms don't work well with raw, unprocessed data.
Before you can train effective models, you need to transform your data into a format that algorithms can understand and learn from:
- How do you handle features with different scales?
- What happens when you have too many dimensions?
- How do you deal with datasets where one class dominates?
This part of the book dives deep into the critical preprocessing steps that turn messy, real-world data into clean, model-ready features.
Data engineering and preprocessing: where data science meets machine learning.
What You’ll Master in This Part¶
- Feature scaling techniques (StandardScaler, MinMaxScaler) and when to apply them
- Dimensionality reduction methods (PCA) for handling high-dimensional data
- Strategies for dealing with imbalanced datasets (SMOTE, class weights, resampling)
- Integration of preprocessing steps into scikit-learn pipelines
- Best practices for data transformation and feature engineering
Chapter Breakdown¶
| Chapter | Title | What You’ll Learn |
|---|---|---|
| 16 | Feature Scaling and Transformation | StandardScaler, MinMaxScaler, when to scale, pipeline integration |
| 17 | Dimensionality Reduction | PCA mathematics, scikit-learn implementation, visualization, pipeline usage |
| 18 | Dealing with Imbalanced Datasets | Class imbalance concepts, SMOTE oversampling, class weights vs resampling |
Why This Part Matters¶
Raw data is rarely ready for machine learning algorithms. Features might have different scales, datasets might be imbalanced, or you might have hundreds of dimensions that slow down training and hurt performance.
But beyond technical necessity, this part helps you:
- Build more accurate and efficient models
- Understand why certain preprocessing steps are crucial for specific algorithms
- Create reproducible ML pipelines that handle data transformation automatically
- Avoid common pitfalls that lead to poor model performance
- Gain intuition about how data characteristics affect learning algorithms
Bottom line: Great algorithms deserve great data.
This part will show you how to prepare your data so your models can reach their full potential.