Car Insurance Telematics Risk Assessment
Welcome to an in-depth look at the Car Insurance Telematics Risk Assessment project. This project involves developing a comprehensive machine learning system to predict insurance claim probability and severity using telematics data collected from vehicles. The system leverages advanced feature engineering, XGBoost models tuned with Optuna, and a modular inference pipeline to deliver accurate and interpretable risk assessments.
Project Overview
The goal of this project is to utilize telematics data—such as driving behavior, trip characteristics, and environmental factors—to build predictive models that estimate the likelihood and expected cost of insurance claims. The project includes extensive feature engineering, hyperparameter optimization using Optuna, and deployment-ready inference pipelines.
Technologies Used
- Python: Core programming language for data processing and modeling.
- XGBoost: Gradient boosting framework used for both classification and regression models.
- Optuna: Hyperparameter optimization framework to tune model parameters efficiently.
- Pandas & NumPy: Data manipulation and numerical computing.
- Scikit-learn: Utilities for preprocessing, model evaluation, and calibration.
- Jupyter Notebooks: Interactive experimentation and documentation.
Achievements and Impact
- Developed a dual-model system predicting claim probability and severity with high accuracy.
- Implemented advanced feature engineering capturing driving behavior and environmental context.
- Optimized model performance using Optuna hyperparameter tuning.
- Created a modular and reusable inference pipeline for batch and real-time predictions.
Challenges and Solutions
Handling imbalanced claim data and skewed claim amounts were key challenges. These were addressed by using XGBoost’s scale_pos_weight parameter, log-transforming claim amounts, and carefully tuning hyperparameters with Optuna. Feature selection and engineering were critical to improving model generalization and interpretability.
Future Work and Aspirations
Future plans include integrating additional data sources such as weather and traffic, exploring deep learning models for temporal patterns, and deploying the system in a cloud environment for scalable real-time risk assessment.
Explore More Projects
If you're interested in learning more about my work or discussing potential collaborations, feel free to explore more of my projects in the portfolio section or get in touch directly.
Source Code
The full source code for this project is available on GitHub: