feature-engineering

Automate feature engineering for tabular datasets by applying target encoding, interaction terms, and time-based aggregations using pandas, numpy, and scikit-learn to systematically generate features for supervised learning pipelines.

Target encoding for high-cardinality categories replaces categorical values with statistical metrics like mean target values, reducing dimensionality compared to one-hot encoding while preserving predictive information for machine learning models.

Featuretools integrates with pandas to perform automated feature synthesis using Deep Feature Synthesis (DFSynth), automatically generating aggregation and transformation features from relational and tabular datasets.

Create time-based features by extracting temporal components like day, month, year, day_of_week, hour, and quarter from datetime columns in pandas, enabling time-series models to capture seasonality and trends.

Automated feature engineering works for both classification and regression supervised learning tasks, generating interaction features, aggregations, and missing value indicators that improve model performance across different prediction types.

Handle missing values during feature engineering by creating missing value indicator columns and applying imputation-aware feature generation, preserving information about data sparsity for downstream machine learning models.