In this Kaggle competition, participants were asked to combine historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bikeshare program in Washington, D.C. One of the alorithms I tried was the Random Forest algorithm.
For more information, see: https://www.kaggle.com/c/bike-sharing-demand
Matrix correlation of features:
Prediction of bike demand vs ground truth (Random Forest, n = 50 trees)
