Using SVM and Random forest for different features selection in predicting bike rental amount

Abstract

Nowadays, people rely on bike renting service for transportation in short distance to replace walking. It is more convenient and faster for people to transfer from place to place. Public transportation is very popular for people to go to work or school. However, there might not be so many stations to let everyone arrive at the place where they want to go. If it takes too much time from stations to destination, it will make people have less willingness in taking public transportations. Bike renting system like U-bike solves this problem. The need for bike renting leads to a question of setting bike rental locations and the number of bikes in each place, by predicting the number of people renting bikes in each position can make it easier for governments to assign bikes to each position. When predicting the bike rent amount, there are lots of features to consider with, like the weather, time, holiday. Using more features doesn’t mean to be better, so the selection of the feature is essential. In this paper, we proposed a method which will combine random forest and support vector machine to predict the bike rental amount from the last hour. Experiments results will discuss random forest, super vector machine and the combination of the two methods results.