Beginner 12 min read
Data Preprocessing Pipeline
Build robust data preprocessing pipelines for ML.
By Dr. Emily WatsonUpdated March 25, 2026
A well-designed data preprocessing pipeline is the foundation of any successful ML project. This guide shows you how to build robust, reusable pipelines.
Why Preprocessing Matters
Raw data is rarely suitable for direct use in ML models. Preprocessing:
- •Handles missing values
- •Normalizes features
- •Encodes categorical variables
- •Removes outliers
Pipeline Components
A typical pipeline includes:
- •. Data loading and validation
- •. Missing value handling
- •. Feature transformation
- •. Feature selection
- •. Train/test splitting
Handling Missing Data
Common strategies:
- •Drop: Remove rows/columns with missing values
- •Impute: Fill with mean, median, or mode
- •Predict: Use ML to predict missing values
- •Flag: Create indicator variables
Feature Scaling
Most ML algorithms benefit from scaled features:
- •StandardScaler: Zero mean, unit variance
- •MinMaxScaler: Scale to [0, 1] range
- •RobustScaler: Uses median and IQR (handles outliers)
Encoding Categorical Variables
Convert categories to numbers:
- •One-Hot Encoding: For nominal categories
- •Label Encoding: For ordinal categories
- •Target Encoding: For high-cardinality features
Building with Scikit-learn
Use Pipeline and ColumnTransformer for clean, reproducible code: ```python from sklearn.pipeline import Pipeline from sklearn.compose import ColumnTransformer ```
Best Practices
- Fit preprocessing only on training data
- •Save fitted transformers for inference
- •Document all transformations
- •Version your pipelines