Video Introductions, Examples and Structured Learning

Documentation

See the documentation for minimalist instructions on creating streams and submitting predictions. 

FAQ?

It moved to Frequently asked questions. Raise an issue on Github.

Python Video Tutorials

A short structed introduction    

1. Your first submission
2. Your first crawler
3. Retrieving historical data 
4. Creating a data stream
5. Modifying how your crawler makes predictions
6. Modifying where your crawler makes predictions

Python Notebook Examples

The repository contains notebook examples you can easily run (for example just open them in Google Colab).

• Create an identity 
• List current prizes
• Submit a prediction
• Start a crawler
• Retrieve historical data
• Analyze emoji use in real-time
• Assign an email

and more. 

See the documentation

R-Tutorials  

Welcome R statisticians! 

• Your first submission (from Kaggle kernel)

More on the way soon. 

Julia Examples  

Welcome, Julia developers!  Video tutorials are on the way. For now:

• A Julia client for microprediction,
• Examples of creating streams in Julia, and
• Examples of storing history.

More at Rusty's repo including an advanced electricity prediction project. 

Player Strategy

Be sure you understand the mechanics of how predictions are quarantined and rewarded.

• An Introduction to Z-Streams explains mechanics, quarantine and implied percentiles.
• The Lottery Paradox article discusses the accuracy and reward of distributional predictions. 
• The Guide to Crawler Examples includes good jumping off points, including ARIMA, ESN, NN or wacky filters for noisy data.  
• Our Listing of Python Time Series Packages ranks prediction, outlier, classification, causality, copula, change-point, matrix-profiling, distribution fitting, hyper-parameter optimization, back-testing libraries and more - all ranked by downloads. 
• Read How to Enter a Cryptocurrency Copula Contest to better understand the motivations. 

Business Context

For those who may be interested, and willing to invest the time, here is a sequence of talks providing what is hopefully a coherent case for an open prediction network. It isn't an elevator pitch.

Along the way, we explain why vendor Automated Machine Learning and previous attempts at crowdsourcing might violate a fundamental axiom, at least if quantitative business optimization is to be fully democratized. Pro tip: Some people like to start with #3. 

1. A first look at microprediction and how it gives rise to the "ten minute data science project". 
2. What must a microprediction oracle do?  The answer isn't Kaggle, or DataRobot. 
3. Business uses of microprediction include pretty much everything (unless "prediction" is in the name, ironically). 
4. Repeated value function prediction provides the link between microprediction and Control Theory and Reinforcement Learning, which is why microprediction is truly general. 
5. Why algorithms make excellent managers, and by implication why humans are terrible. Copious theory means algorithms are well placed to orchestrate production of prediction. Humans need not occupy a blocking role.

Learn Data Science

If one person decides to learn to code so they can use the prediction network, we're done. Seriously it isn't that hard. Here are some entirely free ways to get going.

1. Visual Coding and games to get started with coding concepts (my daughter and I love some of these).
2. Learn to Code, in Python if you've never coded before.
3. Learn Python more quickly if you've coded in another language. 
4. Learn data science using Python. 

Suggestions

Please contact us if you can help improve these resources. As noted above we'd love to answer your questions via Gitter, or issues on Github, the discussion on GitHub, or even questions at Quora. You can also email us directly.