Summary  
This chapter introduces feature scaling as a preprocessing technique to normalize feature ranges so that distance-based models like k-NN treat all features equally and improve performance.

General domain of usage  
Machine learning preprocessing

After handling missing values and encoding categorical features, the dataset is free of issues that would cause errors in the model. However, another challenge remains: **different feature scales**.


This problem will not cause errors if you feed the current-state data to the model, but it can **substantially worsen some ML models**.

Consider an example where one feature is `'age'`, ranging from **18** to **50**, and the second feature is `'income'`, ranging from **$25,000** to **$500,000**. It's clear that a ten-year difference in age is more significant than a ten-dollar difference in income.

However, some models, such as **k-NN** (which we will use in this course), may treat these differences as **equally important**. Consequently, the `'income'` column will have a much more significant impact on the model. Therefore, it's crucial for features to have **roughly the same range** for k-NN to function effectively.

While other models may be less affected by different scales, scaling data can significantly **enhance processing speed**. Thus, data scaling is commonly included as a final step in preprocessing.

As mentioned above, data scaling is usually the **last step** of the preprocessing stage. That is because changes to features made after scaling can make the data unscaled again.

Note

The next chapter will cover the three most used transformers for data scaling. Those are `StandardScaler`, `MinMaxScaler`, and `MaxAbsScaler`.

Why is it important to scale features in machine learning models like k-nearest neighbors (KNN)?

Master the fundamentals of Machine Learning and the Scikit-learn library. Explore the complete ML workflow, from handling missing values and encoding categorical data to scaling features. Build efficient, leak-proof data preprocessing pipelines using ColumnTransformer. Transform raw datasets into model-ready structures and implement robust predictive pipelines.

Why Scale the Data?