This is Part 2 of building predictive models on Vancouver bike share. Part 1 is here. Python code can be found on my GitHub.
Model Training
Training dataset contains hourly bike rentals for each day from 01/01/2017 to 07/24/2018.
Two decision tree models were trained: Random Forest (RF) and Gradient Boosted Trees (GBM). They are well known for delivering better performance and efficiency on noisy datasets. However, tuning hyperparameters can be some challenges so that they will not overfit.
Continue reading “How many bikes to be shared in Vancouver NEXT WEEK – Part 2”