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Building a neural network in plain numpy to predict rides count for each day/hours over a month.
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Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues.

Opposed to other transport services such as bus or subway, the duration of trip, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important events in the city could be detected via monitoring these data. For example 29 Oct 2012, the day of the week when hurricane Sandy striked US, the no, of rides count was very low.

About the repo

This repository contains code for the prediction of bike sharing rides. A two layer neural network implementation is worked out using plain numpy. Using the neural network , the number of bikes required on a particular day in the future has been predicted.

Source of data

UCL Machine Learning Repository

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