Data Preprocessing
The short answer · 17 words
In short
Data preprocessing brings raw data reproducibly into a form that is suitable for analysis or machine learning.
Flow-alerta.cab (2026): Data Preprocessing. https://flow-alerta.cab/en/glossar/data-preprocessing. Editorially reviewed on: 10 October 2026.
How much detail would you like?
Practical
Appropriate preprocessing improves technical processability and comparability. It cannot conjure up errors and must be checked together with data quality and objectives.
Missing age values are imputed according to a defined rule, categories are one-hot-coded and numerical characteristics are scaled with parameters determined on training data.
SMEs document each step, preserve raw data and compare impacts on the model and subgroups. A pipeline reduces leakage risks but does not completely prevent them.
At a glance
- Core
- Depending on the task, preprocessing involves cleaning, imputation, scaling, normalization, coding and transformation. Steps can take place before or within a model pipeline. Learned parameters must only come from the respective training data.
- Use
- Appropriate preprocessing improves technical processability and comparability. It cannot conjure up errors and must be checked together with data quality and objectives.
- Example
- Missing age values are imputed according to a defined rule, categories are one-hot-coded and numerical characteristics are scaled with parameters determined on training data.
- Limits
- Adjust transformations before the train test split, use test data for parameter selection or remove outliers without technical examination. Data cleanup and feature engineering overlap, but are not identical.
In context
Questions about this term
When is it fitting?
Only on the training dataset or training fold.
Is scaling always necessary?
No, depending on the model.
Is pre-processing the same as cleansing?
Cleaning up is a possible part of that.
All details and background
Depending on the task, preprocessing involves cleaning, imputation, scaling, normalization, coding and transformation. Steps can take place before or within a model pipeline. Learned parameters must only come from the respective training data.
ELI5 – Explained simply
Before a model learns, inconsistent information is ordered, missing values are treated and sizes are brought into a comparable form.
Why it matters in practice
Appropriate preprocessing improves technical processability and comparability. It cannot conjure up errors and must be checked together with data quality and objectives.
Example
Missing age values are imputed according to a defined rule, categories are one-hot-coded and numerical characteristics are scaled with parameters determined on training data.
Pitfalls and limits
- Adjust transformations before the train test split, use test data for parameter selection or remove outliers without technical examination. Data cleanup and feature engineering overlap, but are not identical.
Origin & history
Preprocessing has been a central step since statistics and machine learning. Libraries today bundle transformations into reproducible pipelines.
Use in mid-sized companies
- SMEs document each step, preserve raw data and compare impacts on the model and subgroups. A pipeline reduces leakage risks but does not completely prevent them.
Related terms
- Feature Engineering
- Imputation
- Scaling
- Data leakage
- scikit-learn pipeline
Matching Flow-alerta service
AI opportunity analysis & roadmapAI data preparation and quality assurance: develop reproducible, leakage-poor pre-processing and tests for productive pipelines.Matching tool / use case
View use casesscikit-learn Pipeline and ColumnTransformer; Use Case: Prepare mixed numerical and categorical order data for a forecasting model.Sources
- scikit-learn.orgscikit-learn, „Preprocessing data“ (Abruf 06.10.2026)
Quellenprüfung: 10.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.
Editorially reviewed on: