Can you predict the required number of bikes for rental given information about the weather and time of the day?
You are provided hourly rental data along with weather data. For this competition, the training set is comprised of the first 20 days of each month, while the test set is the 21th 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.
- train.csv - the training set.
- test.csv - the test set.
- sample_submission.csv - a sample submission file in the correct format
- ID - an ID for this instance
- Date - year-month-day
- Hour - Hour of he day
- Temperature - Temperature in Celsius
- Humidity - %
- Windspeed - m/s
- Visibility - 10m
- Dew point temperature - Celsius
- Solar radiation - MJ/m2
- Rainfall - mm
- Snowfall - cm
- Seasons - Winter, Spring, Summer, Autumn
- Holiday - Holiday/No holiday
- Functional Day - NoFunc(Non Functional Hours), Fun(Functional hours)
- y - Rented Bike count (Target), Count of bikes rented at each hour
The evaluation metric for this competition is Root Mean Squared Log Error RMSLE, which is calculated as follows.
Seoul Bike Rental Prediction - AI-Pro - ITI
The dataset provided in this competition is obtained from UC Irvine Machine Learning Repository - Seoul Bike Sharing Demand Dataset.
This competition is for educational purposes only.