A Light Gradient Boosting Machine Regression Model for Prediction of Agriculture Insurance Cost over Linear Regression

To increase accuracy for the prediction of agriculture insurance claim cost based on crop data.Gradient Boosting Machine (LGBM) and linear regression models are tested with total Samples 6022 n=7 iterations to predict accuracy. LGBM works decision tree algorithm fitted equation. The coefficient determination values proposed (92.52%) (72.47%) obtained. There was a statistical significance between (p=0.001).Prediction technique produces significantly better performance than technique.