Prediction of weather using high-performance gradient boosting

Our weather prediction technology is imprecise despite its many new uses. Thus, demand exists to adopt a new method that eliminates the system's drawbacks and accurately projects rain. Existing machine learning methods use more RAM, are hard to trim, take a long time to compute, and are hard to use for time series predicting datasets. A high-performance gradient-boosting framework-based decision tree algorithm predicts rain. We used light gradient boosting machine (Light GBM), a leaf-wise method with best-fitting models that eliminates overfitting better than other decision tree algorithms. Predicting continuous goal variables is faster, more efficient, and uses less memory. Rain is Seattle's trademark. This study uses the Seattle dataset of daily weather from 1948 to 2017. The goal is to compute DATE, PRCP, TMAX, TMIN, and RAIN at each break and create a final forecast based on the sampled light BGM that is more accurate than other boosting algorithms.