Using Data to Predict Bike Share Demand In London Without Code

Written by jackriewe | Published 2019/12/08
Tech Story Tags: no-code-platform | nocode-tutorials | machine-learning | data-science | nocode-movement | data-predictions | datascience-workflow | hackernoon-top-story

TLDR Using machine learning to predict demand using no-code machine learning, we created the ideal day when the number of bike-share rentals was the highest in London. We used a dataset that collected data over a year-long period to identify what season the bike share rentals are the highest. Users are more likely to use other forms of transportation (Uber, train, bus) to avoid bad weather. The ideal day for bike share rental is 9.5°C on a non-holiday weekday with a wind speed of 20 km/h.via the TL;DR App

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Written by jackriewe | A content creator who covers ways to be creative with AI and ML.
Published by HackerNoon on 2019/12/08