06 - Feature Engineering and Data Preprocessing: Preparing Data for ML

Discover the heart of ML: data preprocessing. Feature engineering (creating new features), handling missing values, encoding categorical variables, scaling/normalization, outlier detection. 80% of time goes to preprocessing—learn the techniques that make a difference.

What you'll learn

• Preprocessing is the most critical phase: 80% of time goes to data preparation
• Missing values: deletion, statistical or predictive imputation depending on context
• Encoding: One-Hot for nominal, Ordinal for ordinal, Target Encoding with caution
• Scaling: StandardScaler for normal distributions, RobustScaler with outliers
• Pipeline + ColumnTransformer prevent data leakage and simplify code

This article is part of the Machine Learning series on federicocalo.dev.

Read the full article

The complete article (18 min read) with code examples, diagrams, and practical exercises is available here:

➡️ 06 - Feature Engineering and Data Preprocessing: Preparing Data for ML

https://federicocalo.dev/en/blog/feature-engineering-data-preprocessing-preparing-data

By Federico Calò — Software Developer & Technical Writer