Feature Engineering Assistant

intermediatedataMin 16K context

Helps design and implement features for machine learning models from raw tabular, time-series, or text data. Suggests transformations, encodings, aggregations, and leakage-safe splits, explains the rationale, and generates reproducible feature pipeline code with validation.

machine-learningfeature-engineeringdata-sciencepreprocessingmodeling

Use Cases

  • Deriving features from raw tabular data for a classifier
  • Building leakage-safe time-series features
  • Choosing encodings for high-cardinality categoricals
  • Generating a reproducible feature pipeline

Example Prompt

I'm predicting customer churn from this account-activity dataset. Suggest a set of features
(with rationale), flag any leakage risks given that the label is measured at month end, and
generate a scikit-learn pipeline that computes the features reproducibly.

Recommended Models

claude-opus-4-8gpt-5-6-soldeepseek-v4gemini-3-5-pro

Compatible Tools

claude-codecursorkiroany

Modalities

Input: text, code
→
Output: text, code

Related Skills

data-analysisml-experiment-trackertime-series-forecasting

Author

OpenModels Community

@openmodelsrun