Predicting Bike Rentals - Dataquest Project
In this project we use a bike sharing data from Washington, D.C. The District collects detailed data on the number of bicycles people rent by the hour and day. The data is made available by the University of California, Irvine's website
It is a CSV file containing following columns:
- instant - A unique sequential ID number for each row
- dteday - The date of the rentals
- season - The season in which the rentals occurred
- yr - The year the rentals occurred
- mnth - The month the rentals occurred
- hr - The hour the rentals occurred
- holiday - Whether or not the day was a holiday
- weekday - The day of the week (as a number, 0 to 7)
- workingday - Whether or not the day was a working day
- weathersit - The weather (as a categorical variable)
- temp - The temperature, on a 0-1 scale
- atemp - The adjusted temperature
- hum - The humidity, on a 0-1 scale
- windspeed - The wind speed, on a 0-1 scale
- casual - The number of casual riders (people who hadn't previously signed up with the bike sharing program)
- registered - The number of registered riders (people who had already signed up)
- cnt - The total number of bike rentals (casual + registered)
- We will try to predict the total number of bikes people rented in a given hour - column cnt - using all other columns besides casual and registered as these sum up to cnt.
The file contains 17380 rows, with each row representing the number of bike rentals for a single hour of a single day.