Live Case – Project

Help build the open source prediction network

A Collaboration Between

Engagement Synopsis

We’ve built a network of real-time data and algorithms to make low-cost, high-quality predictions accessible and useful. Getting predictions is as simple as publishing data to our platform where competing, state-of-the art algorithms converge on optimized predictions.

Micropredictions LLC is a subsidiary of Intech Investments, a top five U.S. Equity fund manager by a variety of metrics. Micropredictions LLC is established to foster development of the world’s first microprediction network and its use by organizations large and small. We support the open source software development both directly, and through contributor incentives. We host the first node at www.microprediction.org, where you will find an API can be used to predict literally anything. We help companies or organizations get up to speed with usage patterns, including privacy preserving use of public prediction, and the techniques linking repeated prediction and business optimization.

In this project, student would learn how to write and deploy programs that directly address real-time operational problems. These would predict quantities such as electricity (https://www.microprediction.com/competitions/electricity) or cryptocurrency movements.

Students would also be able to create intelligent applications, such as a web page, that uses the turnkey prediction API to drive intelligence, thus pioneering new ways to exploit cheap prediction.

Company Information

Company
HQN/A
RevenueN/A
EmployeesN/A
StageN/A
Hiring PotentialN/A
Website

Company Overview

N/A

Course Info & Engagement Details

School
Engagement FormatLive Case - Class Collaboration or Case Competition - This learning format allows educators to deliver experiential learning to students at scale. Students are often split into groups to work on a live case (or a series of cases) from a real host company that directly relates to key learning objectives.
CourseSpring Virtual Data Science Consulting Project
Level
  • All Undergraduate
Students Enrolled8
Meeting Day & TimeMonday, Wednesday, Friday: 11:00 AM MT - 11:50 AM MT
Student Time Commitment8-15 Hours Per Week
Company Time Commitment2 Hours
Duration13.57 Weeks

Project Topics

Data Management

Product Design & Development

Research & Development

Software Design & Development

Students

There are currently no students assigned.

Collaboration Timeline

  • November 30, 2020

    Deadline for Industry Partners to Apply to Participate

  • December 14, 2020

    Project Scope Finalized

  • January 18, 2021

    OFFICIAL PROJECT LAUNCH: We’ll find a time on this day to web conference you into our class to kickoff the project.

  • April 23, 2021

    OFFICIAL PROJECT END: We’ll find a time on this day to web conference you into our class to close the project.

Key Milestones & Project Process

  • February 27, 2021 - Deep-dive into Micropredictions site and its features and functionalities

    • What are the central objectives for the site?
    • What features and functionalities are available?
    • How does the site operate, and how does the site facilitate the user’s engagement with the open microprediction network? For instance:
      • How does the user pull historical data and launch an algorithm?
      • What data are available live stream? How is the data accessed, monitored and utilized?
      • How is Python used in this context?

    Suggested Deliverable:

    • As they proceed through the Python tutorials
      1. Create an identity (MUID)
      2. Enter a “z1” contest in a one-off fashion by estimating a univariate distribution
      3. Run the default crawler
      4. Create a data stream for others to predict, using an interesting source of live data that updates every 15 minutes (e.g. transport)
      5. Use an open source Python package to improve the prediction model in the crawler
      6. Modify the crawler to focus on certain types of streams
  • April 23, 2021 - Create and deploy a well-documented and useful quantile estimation open source library

    • Research and collect background material and data sources for online quantile estimation found in open source world 
    • Write and deploy quantile algorithm for estimation  
    • Find relevant exogenous data for the deployed model, such as humidity impacting electricity consumption.
    • Create user-friendly open source library framework and populate with background material and data sources
    • Learn how to deploy a package to PyPI https://www.linkedin.com/pulse/you-love-your-algorithm-set-free-peter-cotton-phd/

    Suggested Deliverable:

    • Open source library for online quantile estimation
    • Blog article featured on Micropredictions website on the subject of online quantile estimation for online analysis and the new open source library

Project Resources

There are no resources currently available

Company Supervising Team

There are currently no supervisors assigned.

School Supervisors

There are currently no supervisors assigned.