Short answer: what does normalize data mean? Making numbers play nice together.
Longer answer: It’s about fairness. If one feature ranges 0-1000 and another 0-1, your algorithm might unfairly prioritize the bigger numbers. Normalization fixes that.
How? Min-max (0 to 1) or z-score (mean=0, std=1). Use `StandardScaler` in sklearn—it’s like three lines of code.
Pro tip: Always normalize for neural networks or gradient descent. For tree-based models? Meh, not always needed.
Longer answer: It’s about fairness. If one feature ranges 0-1000 and another 0-1, your algorithm might unfairly prioritize the bigger numbers. Normalization fixes that.
How? Min-max (0 to 1) or z-score (mean=0, std=1). Use `StandardScaler` in sklearn—it’s like three lines of code.
Pro tip: Always normalize for neural networks or gradient descent. For tree-based models? Meh, not always needed.