/Bike_Sharing_Demand

predict bike sharing demand using the AWS AutoGloun Framework

Primary LanguageJupyter Notebook

Bike_Sharing_Demand

kaggle competition: https://www.kaggle.com/c/bike-sharing-demand, predict bike sharing demand using the AutoGluon Framework and by perfoming feature engineering to the data.

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 rent a bike from a 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.

This dataset was provided by Hadi Fanaee Tork in the kaggle platform.

Model by Pedro Herrera.