Note: built with a team [Rionel Dmello, Allan Wu]
This was part of a Kaggle competition that utilizes the dataset from the UC Irvine Center for Hydrometereology and Remote Sensing. Our goal was to predict whether there
was rain at a certain location. Our final result consisted of an ensemble method that combined Random Trees, Adaboost with Decision Trees, and Extra Trees. The final report
explains in detail our thought process and our reasons for what we did. Our group ended up getting 78.56% accuracy, scoring a final place of 46 out of 161. Provided is the code
for the Random Forest Classifer.
The results of all the classifiers are as follows:
