What is data normalization and standardization?
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Data normalization and standardization are techniques used to scale numerical data so that features are on a similar scale, which improves the performance of machine learning models.
✅ Normalization
Normalization scales data to a range between 0 and 1 (or -1 to 1).
Formula:
x_normalized = (x - min) / (max - min)
Use Case:
Useful when features have different ranges and you want to bring them to a common scale without distorting differences.
Example:
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
normalized_data = scaler.fit_transform(data)
✅ Standardization
Standardization scales data to have a mean of 0 and standard deviation of 1.
Formula:
x_standardized = (x - mean) / std
Use Case:
Best when data follows a normal distribution. Often used in linear models, SVMs, PCA, etc.
Example:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
standardized_data = scaler.fit_transform(data)
🔍 Summary:
Normalization: Rescales to [0,1]; use for bounded features.
Standardization: Centers data; use for normally distributed data.
Both help models converge faster and perform better by ensuring features contribute equally.
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