A data science project developing a parametric insurance model to provide financial protection against weather-related risks such as excess rainfall and drought.
This project involves the development of a Parametric Insurance Solution aimed at providing financial protection against weather-related risks. The solution uses advanced analytics to trigger automated payouts based on predefined weather conditions.
File: 01-index-setup.ipynb
Initializes risk indices and baseline configuration for the insurance model. Sets up key parameters and indices needed for analysis.
File: 02-model-training.ipynb
Develops and trains the AI/ML model using historical weather and insurance data. Implements algorithms for predictive modeling.
File: 03-data-processing.ipynb
Multi-phase data engineering pipeline. Cleans, transforms, and engineers features from raw weather and risk data.
File: 04-payout-triggers.ipynb
Designs and optimizes trigger mechanisms and threshold settings. Determines when automated payouts are triggered based on weather conditions.
- Python - Core programming language
- Jupyter Notebooks - Interactive development
- Pandas - Data manipulation
- NumPy - Numerical computations
- Scikit-learn - Machine learning
- Matplotlib/Seaborn - Visualization
✓ Data Analysis & Exploration ✓ Machine Learning & AI ✓ Feature Engineering ✓ Statistical Modeling ✓ Python Programming ✓ Insurance Domain Knowledge ✓ Multi-phase Data Processing
Organization: WRMS (Weather Risk Management Solutions), Gurgaon
Objective: Develop automated weather-triggered insurance for farmers
- Clone the repository
- Open Jupyter Notebook
- Run notebooks in order:
- Start with
01-index-setup.ipynb - Then
02-model-training.ipynb - Then
03-data-processing.ipynb - Finally
04-payout-triggers.ipynb
- Start with
- Follow the detailed comments in each notebook
Built with Python | Machine Learning | Insurance Analytics