# 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.*

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## 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)**

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

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*By [Federico Calò](https://federicocalo.dev) — Software Developer & Technical Writer*