Kaggle.com hosts a lot of interesting machine learning problems online and thousands of its members compete to solve them for a bounty. Problems hosted on Kaggle has varying complexity to accomodate newbies to rock star developers – few problems are good enough for  newbies to learn basics of machine learning and few of them challenge the best of machine learning developers.

I’m learning basics of machine learning for the past few weeks and had an opportunity to solve Kaggel’s Bike Sharing Demand problem. Bike Sharing systems allows customers to rent a bike (or a cycle as it is called in many part of the world) for several hours and return them back . The problem provides historical information about the demand for bike sharing business and we need to forecast the demand. For more information on the problem, visit Kaggle.com website.

Here is the solution I written using random forests algorithm using R programming language and you can download the source code from github.  With this solution I was able to score RMSLE of 0.70117, which placed me somewhere in the mid of the leader board.  This is the best score I could get by spending 4 hours of my time. Please feel free to fork the code and improve it.

Get Kaggle Bike Sharing Demand solution code from GitHub