Feature Scaling in Data Science – Normalization vs Standardization
In this session, we explore feature scaling, one of the most important preprocessing steps in data science and machine learning. Scaling ensures that features are treated fairly and prevents large-scale variables from dominating smaller ones, improving both model performance and interpretability.
We cover the two most widely used techniques:
1) Normalization (Min-Max Scaling): Rescales features into a fixed range (0–1). Best for neural networks and distance-based algorithms like k-nearest neighbors and clustering.
2) Standardization (Z-score Scaling): Transforms data to have mean = 0 and standard deviation = 1. Ideal for algorithms assuming normal distributions such as linear/logistic regression, PCA, and SVMs.
📊 Key Comparisons:
Normalization → fixed range, sensitive to outliers
Standardization → no fixed range, less sensitive to outliers
Normalization = best for deep learning & distance-based models
Standardization = best for regression, PCA & Gaussian-based models
By the end of this module, you’ll understand when to use normalization vs. standardization, and how each impacts your machine learning workflows.
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