Abstract
In the paper, the work focuses on weather prediction by using real time data from day to day. Weather Prediction has proven to be a very important applica- tion of Machine Learning since the beginning. Different models were studied and found out ways how prediction could be made more accurate by aban- doning the classical models and adopted a hybrid method of including more than hundred decision trees bagged to form an aggregate total. The aggregate results achieved from each tree was considered to be a random split of data, saving a lot of computation time. Gradient Boosting was used to increase accu- racy significantly making it a very efficient model to work with. The boosting helped the weak learner Decision Tree to select a random sample of data, fit it with a model and train it sequentially to compensate for the weakness of its predecessor. To improve the accuracy of a model in boosting, a combination of a convex loss function, which measures the gap between the expected and goal outputs, and a penalty term for the complexity of the model were used to reduce a regularized objective function that included both L1 and L2 regression tree functions. The resulting model achieved a significantly high level of accuracy when tested with new data.
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