Data preprocessing is the work of cleaning, normalizing, and transforming raw data into a form a machine learning model can actually use.
It covers fixing messy text, scaling numbers, and encoding categories—the quiet decisions that often determine whether a model succeeds or fails. Also known as: Data Cleaning, Data Wrangling
What this topic covers
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MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.
Concepts covered
Split data into train and test sets before preprocessing to prevent data leakage. Fitting scalers on the full dataset inflates accuracy and fails in production.
Data leakage occurs when information unavailable at prediction time enters training, inflating validation accuracy while production performance collapses.
Data preprocessing cleans, scales, and encodes raw data into model-ready features. Fitting transformers before the train-test split causes data leakage.
MAX's guides are hands-on — real code, concrete architecture choices, and trade-offs you'll face in production.
Tools & techniques
scikit-learn pipelines stop data leakage by fitting transformers on training data only. ColumnTransformer routes numeric and categorical columns separately.
DAN tracks how this domain is evolving — which models, techniques, and benchmarks are reshaping 2026.
Models & benchmarks
Updated June 2026
Data-prep tooling consolidates on Apache Arrow in 2026: pandas 3.0, Polars, and RAPIDS cuDF interoperate zero-copy. Leakage-safe pipelines are now default.
ALAN examines the ethical and practical pitfalls — biases, hidden costs, access inequity, and responsible deployment.
Risks & metrics
Dropping rows with missing values erases minority groups, who carry more missing data. Preprocessing decisions encode bias before a model trains.