BIKE_RENTING_R_PYTHON

The objective of this project is to predication of bike rental count on daily based on the environmental and seasonal settings. As it gets easy for an organisation to arrange the resource if the demand spikes.

Attribute Information:

1. instant: Record index
2. dteday: Date
3. season: Season (1:springer, 2:summer, 3:fall, 4:winter)
4. yr: Year (0: 2011, 1:2012)
5. mnth: Month (1 to 12)
6. holiday: weather day is holiday or not (extracted fromHoliday Schedule)
7. weekday: Day of the week
8. workingday: If day is neither weekend nor holiday is 1, otherwise is 0.
9. 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
10. temp: Normalized temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min)(only in hourly scale)
11. atemp: Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_maxt_min)
12. hum: Normalized humidity. The values are divided to 100 (max)
13. windspeed: Normalized wind speed. The values are divided to 67 (max)
14. casual: count of casual users
15. registered: count of registered users
16. cnt: count of total rental bikes including both casual and registered

Programming Language : Python 3 and R Language

Note:

Please refer the project report