Many American cities have communal bike sharing stations where you can rent bicycles by the hour or day. Washington, D.C. is one of these cities. The District collects detailed data on the number of bicycles people rent by the hour and day.
Hadi Fanaee-T at the University of Porto compiled this data into a CSV file, which you'll be working with in this project. The file contains 17380 rows, with each row representing the number of bike rentals for a single hour of a single day. You can download the data from the University of California, Irvine's website.
Here are the descriptions for the relevant 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)
I will try to predict the total number of bikes people rented in a given hour. I'll predict the count column using all of the other columns, except for casual and registered because that is a potential column for data leakage and apparently it is not present in test data as well