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Practice

Feature Scaling and Preprocessing


In machine learning, feature scaling and preprocessing make sure all features contribute equally to the model, and the data is in the right format for learning.

Without scaling, models like KNN or gradient descent-based algorithms can be biased toward features with larger numeric ranges.


Common Preprocessing Steps​

  • Feature Scaling – Normalize or standardize values so they’re on a similar scale.
  • Encoding Categorical Variables – Convert text labels into numbers.
  • Handling Missing Values – Replace or remove nulls.
  • Feature Transformation – Apply mathematical transformations (log, polynomial, etc.).

Example: Standardization and Normalization​

Scaling Features in Scikit-learn
# Install scikit-learn in Jupyter Lite
import piplite
await piplite.install('scikit-learn')

import numpy as np
from sklearn.preprocessing import StandardScaler, MinMaxScaler

# Example dataset
X = np.array([[1.0, 200.0],
              [2.0, 300.0],
              [3.0, 400.0]])

# Standardization (mean=0, std=1)
scaler_std = StandardScaler()
X_std = scaler_std.fit_transform(X)

# Normalization (range [0, 1])
scaler_mm = MinMaxScaler()
X_mm = scaler_mm.fit_transform(X)

print("Standardized Data:\n", X_std)
print("\nMin-Max Scaled Data:\n", X_mm)

Choosing the Right Scaling Method​

  • Standardization – Best for algorithms assuming Gaussian-like distributions (e.g., logistic regression, SVM).
  • Normalization – Best for distance-based models (e.g., KNN, neural networks).

Key Takeaways​

  • Always scale numeric features when using algorithms sensitive to scale.
  • Perform preprocessing after splitting into train/test sets to avoid data leakage.
  • Scikit-learn’s Pipeline helps combine preprocessing with model training.

What’s Next?​

In the next lesson, we’ll have a mid-chapter quiz to review your understanding of supervised learning basics.

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