BIKE SHARING DEMAND (A Kaggle Dataset)

INTRODUCTION

Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able rent a bike from a one location and return it to a different place on an as-needed basis. Currently, there are over 500 bike-sharing programs around the world.

The data generated by these systems makes them attractive for researchers because the duration of travel, departure location, arrival location, and time elapsed is explicitly recorded. Bike sharing systems therefore function as a sensor network, which can be used for studying mobility in a city. In this competition, participants are 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.

PROBLEM STATEMENT

In this project, you are asked to combine historical usage patterns with weather data in order to forecast hourly bike rental demand.

DATA

You are provided with following files:

1. train.csv : Use this dataset to train the model. This file contains all the weather related features as well as the target variable “count”. Train dataset is comprised of first 18 months.

2. test.csv : Use the trained model to predict the count of total rentals for each hour during the next 6 months.

Here is the description of all the variables :

datetime: hourly date + timestamp

season: Type of season (1 = spring, 2 = summer, 3 = fall, 4 = winter)

holiday: whether the day is considered a holiday

workingday: whether the day is neither a weekend nor holiday

weather: weather

temp: temperature in Celsius

atemp: "feels like" temperature in Celsius

humidity: relative humidity

windspeed: wind speed

casual: number of non-registered user rentals initiated

registered: number of registered user rentals initiated

count: number of total rentals

ACKNOWLEDGEMENT

I collected this dataset from the Kaggke website and I would like to thank them for posting this dataset for much needed practical exposure in Machine Learning. This dataset was provided by Hadi Fanaee Tork using data from Capital Bikeshare. I would also like to thank the UCI machine learning repository for hosting the dataset.

REFERENCES

1. https://www.kaggle.com/c/bike-sharing-demand

2. Fanaee-T, Hadi, and Gama, Joao, Event labeling combining ensemble detectors and background knowledge, Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg.