The objective of this Case is to Prediction of bike rental count on daily basis on the environmental and seasonal settings.
A bike rental is a bicycle business that rents bikes for short periods of time.
Most rentals are provided by bike shops as a sideline to their main businesses of sales and
service, but some shops specialize in rentals.
Bike rental shops rent by the day orweek as well as by the hour, and these provide an excellent opportunity for people
who don't have access to a vehicle, typically travelers and particularly tourists.
Specialized bike rental shops thus typically operate at beaches, parks, or other
locations that tourists frequent. In this case, the fees are set to encourage renting the
bikes for a few hours at a time, rarely more than a day.
##The objective of this Case is to predict the bike rental count based on the environmental and seasonal settings, Sothat required bikes would be arranged and managed by the shops according to environmental and seasonal conditions.
Our task is to build regression models which will predict the count of bike rented depending on various environmental and seasonal conditions Given below is a
sample of the data set that we are using to predict the count of bike rents:
Variables present in given dataset are instant, dteday, season, yr, mnth, holiday,weekday, workingday, weathersit, temp, atemp, hum, windspeed, casual,registered, cnt
The details of variable present in the dataset are as follows -
instant: Record index
dteday: Date
season: Season (1:springer, 2:summer, 3:fall, 4:winter)
yr: Year (0: 2011, 1:2012)
mnth: Month (1 to 12)
hr: Hour (0 to 23)
holiday: weather day is holiday or not (extracted fromHoliday Schedule)
weekday: Day of the week
workingday: If day is neither weekend nor holiday is 1, otherwise is 0.
weathersit: (extracted fromFreemeteo)
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:
Normalized temperature in Celsius. The values are derived via
(t-t_min)/(t_max-t_min),
t_min=-8, t_max=+39 (only in hourly scale)
atemp: Normalized feeling temperature in Celsius. The values are derived via
(t-t_min)/(t_maxt_min),
t_min=-16, t_max=+50 (only in hourly scale)
hum: Normalized humidity. The values are divided to 100 (max)
windspeed: Normalized wind speed. The values are divided to 67 (max)
casual: count of casual users
registered: count of registered users
cnt: count of total rental bikes including both casual and registered