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June 6, 2026 · 1 min read

How would you design a machine learning pipeline in Scikit-learn that can handle both numerical and categorical data efficiently?

To handle both numerical and categorical data, I would use the ColumnTransformer from Scikit-learn to preprocess each type separately, applying appropriate transformations like StandardScaler for numerical features and OneHotEncoder for…

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SOFTWARE_ARCHITECT // AI_ENGINEER

📅 Jun 06, 2026 ⏱ 1 min read

HW

How would you design a machine learning pipeline in Scikit-learn that can handle both numerical and categorical data efficiently?

COVER // HOW WOULD YOU DESIGN A MACHINE LEARNING PIPELINE IN SCIKIT-LEARN THAT CAN HANDLE BOTH NUMERICAL AND CATEGORICAL DATA EFFICIENTLY?

To handle both numerical and categorical data, I would use the ColumnTransformer from Scikit-learn to preprocess each type separately, applying appropriate transformations like StandardScaler for numerical features and OneHotEncoder for categorical features before combining them in a final pipeline.

data preprocessing machine learning python scikit-learn

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