Identify the data fields to normalize
Choose the normalization method
Min-max scale values to a fixed range
Standardize values to zero mean and unit variance
Apply log transformation to reduce skewness
Use robust scaling for outlier-heavy data
Normalize each feature consistently
Fit the scaler on training data only
Transform validation and test data with the same scaler
Handle missing values before normalization
Verify the normalized data range or distribution
Store the normalization parameters for reuse