10 Essential Data Cleaning Techniques Every AI Project Needs
The article outlines ten practical data‑cleaning methods—covering missing‑value imputation, duplicate handling, outlier detection, normalization, discretization, text cleaning, type conversion, multi‑source alignment, feature engineering, and sensitive‑data masking—explaining why each step matters for reliable AI model training.
