Description

Get started on this competition through Kaggle Scripts.

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 to rent a bike from 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 are 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.

Acknowledgements

Kaggle is hosting this competition for the machine learning community to use for fun and practice. This dataset was provided by Hadi Fanaee Tork using data from Capital Bikeshare. We also thank the UCI machine learning repository for hosting the dataset. If you use the problem in publication, please cite:
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.

Evaluation

Submissions are evaluated on the Root Mean Squared Logarithmic Error (RMSLE). The RMSLE is calculated as: 

\[ \sqrt{\frac{1}{n} \sum_{i=1}^n (\log(p_i + 1) - \log(a_i + 1))^2} \]

Where:
- \(n\) is the number of hours in the test set
- \(p_i\) is your predicted count
- \(a_i\) is the actual count
- \(\log(x)\) is the natural logarithm 

Submission Format

Your submission file must have a header and should be structured in the following format:
```
datetime,count
2011-01-20 00:00:00,0
2011-01-20 01:00:00,0
2011-01-20 02:00:00,0
...
...
```

Dataset Description

See, fork, and run a random forest benchmark model through Kaggle Scripts. You are provided hourly rental data spanning two years. For this competition, the training set is comprised of the first 19 days of each month, while the test set is the 20th to the end of the month. You must predict the total count of bikes rented during each hour covered by the test set, using only information available prior to the rental period.

Data Fields
- `datetime`: hourly date + timestamp
- `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`:
  1. Clear, Few clouds, Partly cloudy, Partly cloudy
  2. Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
  3. Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
  4. Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog
- `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