dhiseesunil / forecast-hourly-bike-rental-demand

Forecasting hourly bike rental demand by combining historical usage patterns with weather data using Linear Regression Algorithm.

Forecast-Hourly-Bike-Rental-Demand

Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able to rent a bike from one location and return it to a different place on an as-needed basis. Currently, there are over 500 bike-sharing programs around the world. The data generated by these systems makes them attractive for researchers because the duration of travel, departure location, arrival location, and time elapsed is explicitly recorded. Bike sharing systems therefore function as a sensor network, which can be used for studying mobility in a city.

Problem Statement

In this project, I have combined historical usage patterns with weather data in order to forecast hourly bike rental demand.

Data

The following files are attached in this repository : 1. train.csv : Use this dataset to train the model. This file contains all the weather related features as well as the target variable “count”. Train dataset is comprised of first 18 months. 2. test.csv : Use the trained model to predict the count of total rentals for each hour during the next 6 months.

Data Dictionary

Here is the description of all the variables :

Variable Definition

1. datetime hourly       date + timestamp
2. season                Type of season (1 = spring, 2 = summer, 3 = fall, 4 = winter)
3. holiday               whether the day is considered a holiday
4. workingday            whether the day is neither a weekend nor holiday
5. weather               weather
6. temp                  temperature in Celsius
7. atemp                 "feels like" temperature in Celsius
8. humidity              relative humidity
9. windspeed             wind speed
10. casual               number of non-registered user rentals initiated
11. registered           number of registered user rentals initiated
12. count                number of total rentals

Solution Checker

Use solution_checker.xlsx to generate score (RMSLE) of predictions. This is an excel sheet where its provided with the timestamp and predictions should have to submitted in the count column.