This is a linear regression, decision tree regression and random forest regression project that predicts the number of bikes that will be rented using a dataset containing historical data detailing weather, time and seasons at hourly basis.
The Dataset contains information under various column heads which are explained below:
- instant: record index
- dteday : date
- season : season (1 for spring, 2 for summer, 3 for fall and 4 for winter)
- yr : year (0 for 2011, 1 for 2012)
- mnth : month ( 1 to 12)
- hr : hour (0 to 23)
- holiday : weather day is holiday or not
- weekday : day of the week
- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.
- weathersit : 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.
- atemp: Normalized feeling temperature in Celsius.
- 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
The data is present in the repository in a CSV format or else can be downloaded from here.
The project was done in Jupyter Notebook, Python 3.