Skip to content

Estimate the relationship between the number of bikes shared around the London network on a given day, depending on weather and other characteristics to predict the capacity needed to satisfy demand at any given point. Requires R and familiarity with non-linear regression models

Notifications You must be signed in to change notification settings

StatisticsHealthEconomics/london-bikes

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

5 Commits
 
 
 
 
 
 

Repository files navigation

Predicting the number of bike shares in London

BSc project - 30 credits

Difficulty: Low/medium difficulty 😬 or 😬 😬, depending on specs

Description: We'll use data made publicly available from Transport for London (TFL; source of data: https://www.kaggle.com/datasets/hmavrodiev/london-bike-sharing-dataset) to estimate the relationship between the number of bikes shared around the London network on a given day, depending on weather and other characteristics to predict the capacity needed to satisfy demand at any given point. Requires R and familiarity with non-linear regression models. Students are encouraged to work with Rmarkdown or quarto to develop their dissertation.

About

Estimate the relationship between the number of bikes shared around the London network on a given day, depending on weather and other characteristics to predict the capacity needed to satisfy demand at any given point. Requires R and familiarity with non-linear regression models

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published