The rainfall evaluation model based on XGBoost.

The primary objective of is to bridge the trust gap between farmers and insurance providers, reduce operational costs, and expedite claims settlement, ensuring a more reliable and efficient agricultural insurance system.

• Rainfall Estimation using Machine Learning: Utilizes XGBoost's Gradient Boosted Decision Trees classifier for accurate prediction of rainfall.
• Refined Prediction Mechanism: Enhances predictions using meteorological base station data.
• Zero-Knowledge Proofs: Ensures data integrity and privacy by validating model predictions without revealing underlying data.
• Decentralized System: Transfers loss assessment from insurance companies to a transparent, decentralized system.

The project uses data from the "How Much Did It Rain?" competition on Kaggle, including NEXRAD and MADIS data in CSV format, with over a million samples for training and over 600,000 for testing.

1. Clone the repository.
2. Install required Python packages.
3. Prepare data Download data. Place the train_2013.csv and test_2014.csv files into the input folder.

Model generation and prediction (Roughly 10 hours): Navigate to the code directory:

cd Rain_Forust/code
./main.sh

Once in the code folder, execute the script by typing ./main.sh. Ensure that the following folders are present at the top level of the directory: input, code, processed, models, and output.

• Python 3.x
• XGBoost
• Pandas
• NumPy
• forust
• RISC Zero's zkVM (for zero-knowledge proof validation)

Contributions are welcome. Please fork the repository and submit a pull request with your proposed changes.

For any queries or contributions, please feel free to contact.

• Kaggle's "How Much Did It Rain?" competition data
• Rain_Forus builds on the Devin Anzelmo model and extends the forust-based approach to model training and prediction.