Automated Missing Data Imputation

Purpose

Automated Missing Data Imputation uses ML-based imputation methods, such as KNN and regression, to intelligently fill missing values in datasets so they can be prepared for modeling.

Primary users

The primary user is not specified in the provided information.

Where it fits (process/stage/trigger)

This agent fits into the data preparation stage before model development, when policy-level exposure data and loss experience data contain missing values that must be addressed before the datasets are model-ready.

Key capabilities / workflow

The workflow analyzes the available datasets, identifies missing data, applies ML-based imputation using methods such as KNN or regression, validates whether the imputed dataset is acceptable, and iterates on the imputation approach when refinement is needed.

Inputs

Inputs are not specified beyond the provided datasets, which are policy-level exposure data and loss experience data.

Outputs / Deliverables

The output is a model-ready dataset where missing data has been filled intelligently using ML-based imputation methods.

Value

The agent helps improve dataset readiness for modeling by reducing manual effort in missing data treatment and applying intelligent imputation methods to insurance-related data.