Answer / Pradeep Kumar Sonkar

Data Pre-processing techniques in Machine Learning are essential to clean, transform and normalize raw data before feeding it into a model. Common pre-processing methods include: Normalization (scaling numeric values to a specific range), Imputation (replacing missing data with a value estimate), Feature Extraction (transforming the original data into a different but equivalent form) and Data Cleaning (removing duplicates, outliers or inconsistencies).

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