Predicting Bike Rental Demand

This has been my very first machine learning project. The Kaggle competition “Predicting Bike Rental Demand” is about predicting the number of rented city bikes depending on time and weather. I chose this project because, as in the first lesson of “Machine Learning for Coders: Introduction to Random Forests”, a numerical value should be predicted, and the accuracy should be measured with an RMSLE. So, I could transfer the shown steps and solve the task with a Random Forest Regressor.

First, due to my inexperience, I divided the training set into a training set and a test set as in the lesson, although there was already a separate test set here. Therefore, I had only half of the training data but could calculate the score myself thanks to the actual y-values from the test half. I am very satisfied with the RMSLE of about 0.35 for the first time.

In the second run, I used the data sets as intended and cleaned up mostly formal errors. However, since the competition is already closed, I will not get a score back.

Much more important to me than the result is what I learned in the process:

Be persistent. Talk to your Styrofoam duck, it’s your best friend. Many problems are solved by simply looking at code and data. It’s not witchcraft, just a lot of puzzle work.