Handling Missing Values, Categorical Encoding, and Feature Scaling. Designed for machine learning engineers, the deck balances theoretical intuition with production-ready Python code using pandas and scikit-learn.

Key Learning Objectives:

• Data Integrity: Master strategies for identifying and imputing missing data (Mean, Median, and Mode) without introducing bias.

• Numerical Representation: Understand when to use One-Hot Encoding for nominal data versus Ordinal Encoding for ranked categories.

• Statistical Alignment: Learn the mathematical differences between Standardization (Z-score) and Normalization (Min-Max) and how they impact gradient-based algorithms.

• Best Practices: Avoid common pitfalls like Data Leakage and the Dummy Variable Trap to ensure model generalization.