Data Preprocessing for Neural Network Training

Raw supply chain data is messy: product codes are categorical, demand varies from 0 to millions, some supplier records have missing lead times, dates are inconsistent formats. Neural networks require clean, normalized input. Normalization scales numerical features (e.g., demand 0–1,000,000) to a standard range (0–1 or –1 to 1), ensuring no single feature dominates training due to magnitude. Categorical encoding converts labels (supplier 'ABC Corp') to numeric vectors (one-hot encoding: [1,0,0] for supplier 1 of 3). Missing value handling: drop rows (risky, loses data), fill with mean/median (simple, can bias), or use sophisticated imputation (models the missing pattern). Outliers like…

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