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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

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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

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