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Capital Bikeshare

Machine learning

Products

Timeframe

5 weeks

Tags

product, emergingtech, web

Links

Read Our Lab Notes, View Solution

Challenge

We wanted to explore uses for machine learning, so we decided to create a tool to predict availability of Capital Bikeshare stations depending on location, time of day, and weather.

Solution

We used the large amount of trip data DC's Capital Bikeshare has made available to the public, combined with other historical data, and applied statistical methods. To increase accuracy, we added weather data to the learning system.

Blog Posts

Results

We quickly built a web page and a REST API that collects user input and displays predictions for the given location, date, and time. Next, we decided to add a conversational interface so we can access the API using natural language. We also added a Slack bot integration so that users can access the solution right in the chat window.

Our Latest Projects

Haptic Devices

We created both a haptic compass that buzzes according to the direction of an iPhone and a standalone haptic device to detect iBeacons without the use of a smartphone.

Read Our Lab Notes

Predicting Demographics Using Machine Learning

We tried to predict the gender of celebrities using the IMDB-WIKI project and machine learning.

Read Our Lab Notes
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