Data Preprocessing

Status: public · Confidence: medium (0.725) · Basis: verified_sources

## TL;DR
Data preprocessing turns raw data into inputs that machine-learning estimators can use more reliably. The key public claims are scaling, imputation, and leakage prevention.

## Core Explanation
Common preprocessing steps include feature scaling, missing-value imputation, encoding, and train/test separation. The safest rule is to fit preprocessing steps on training data only, then apply the learned transformation to validation or test data.

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