Claim Prediction Model: This project uses machine learning to predict the likelihood of insurance claims based on policyholder behavior and demographic attributes. Key features include data preprocessing, encoding categorical variables, standardization of numerical features, and model training using Random Forest and Decision Trees.

Automates insurance claim modeling in Kedro: data processing, sampling, feature engineering, feature selection, GLM/LightGBM tuning, recalibration, and validation. Configurable via YAML. Supports generating, scheduling, and tracking hundreds of experiments. Outputs: metrics and charts. Uses Kedro, MLflow, Python.