- How many bikes will be rented today?
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.Try this demo
- Filter Out Stop Words
In computing, stop words are words which are filtered out before or after processing of natural language data (text). Short function words, such as the, is, at, which, and on. This app uses the power of R programming and cloud computing to remove those stop words from your text bodies so that machine learning models can analyze them more efficiently.Try this demo
- Which political party are you more likely to donate to?
When we decide to vote during elections, the outcome is relatively clear. One ballot, one vote towards the candidate chosen. However political involvement can take many different forms beyond voting such as monetary donations, volunteering, campaigning, etc. The impact on politics by individual contributors is more complicated to measure. During the 2014 election cycle, only 0.23% of the United States population gave more than $200 to political candidates, committees, or parties. However, two-thirds of the money that went towards senatorial candidates and half of the money that went towards congressional candidates, came from the 0.23% of the population that contributed. With the raising in cost of election campaign, every single dollar counts.Try this demo
- What was the rating of that review?
Did your user write a review but forget to rate the product? Did the reviewer write one way but rate differently? Review Score Predictor is a web service that aims to predict what the rating may have been based upon the textual syntax of the review itself.Try this demo
- Extract Entities from your text
Text Entity Extractor uses tags to classify elements in text into predefined categories. It can accurately find the names of people, places, and organizations in large bodies of text. It can also find product names, expressions of time, monetary values, and more. Text extracting is used to highlight important pieces of information so you can establish meaning in a body of text without even reading it.Try this demo
- Would you survive the Titanic disaster?
Find out your statistical chances of survival based upon your circumstances to see if you would survive the Titanic disaster. Given your gender, age, fare price, accommodation class, the people you came with you, and the port from which you departed.Try this demo
- Calls an custom AWS machine learning endpoint
This app will talk to your trained AWS Machine Learnnig model to derive a form that you can enter in new records to submit to your model to perform predictions. This app currently only parses and derives schema for classification models.Try this demo
- Simulate a credit card stream
- Turn your phone into an IoT device.
Hooks up your phone's telemetry to the cloud. Takes orientation data (x, y, z) from your phone and sends it into an Azure Event Hub as a stream. Phones use gyroscope, accelerometer, and magnetometer, to detect its orientation for games, apps, and window orientation. This web app will collect the same kind of information a phone app game would, and sends it to an Azure Event Hub.Try this demo
- Stream live tweets
Sends live Twitter stream data from a Twitter API directly into an Azure Event Hub. The business applications for real-time Twitter data is quite versatile. Some examples include tracking tornado damage , high-frequency stock trading , real-time sentiment analysis by geographic location, and brand management portals .Try this demo