Contemporary Analysis

Data Science

Nate Watson

March Machine Learning Mayhem

Machine Learning and the NCAA Men’s Basketball Tournament Methodology

 <<This article is meant to be the technical document following the above article. Please read the following article before continuing.>>

“The past may not be the best predictor of the future, but it is really the only tool we have”

 

Nate Watson

Predicting the upsets for the NCAA Men’s Basketball Tournament using machine learning

Contemporary Analysis (CAN) and Cabri Group and have teamed up to use Machine Learning to predict the upsets for the NCAA Men’s Basketball Tournament. By demonstrating the power of ML through our results, we believe more people can give direction to their ML projects.

Tadd Wood

Why you should update predictive models

After writing my previous post, "How Often Should You Update Predictive Models", it was appropriate to followup with a post regarding the consequences of not updating predictive models.

Grant Stanley

How Often to Update Predictive Models

Everyday new information is being created in your business. Your customers are buying more, subscribing or unsubscribing, and before you know it your customers today are seemingly different than the customer you had the day before.

As these new patterns emerage its important to periodically take time to investigate your data, update your predictive models, and challenge the assumptions about your business going forward. But how often should you do this? To answer that question, consider the following:

  • How often is my data changing?
  • How often do I plan on making decisions with the data?
Grant Stanley

Predictive Analytics is Not a Crystal Ball

Its common to see predictive analytics as a sort of "crystal ball" for your business. This crystal ball image makes for great marketing. Unfortunately, predictive analytics is not a crystal ball.

It will not provide the correct prediction every time. Its primary purpose is to help you make better decisions by giving you the power to unlock the patterns inside your data. When performed correctly this gives you the ability to simplify decisions. When performed incorrectly it can spell disaster for your company.

Predictive analytics is both an art and science. It requires a combination of both empirical and subjective experience to verify that models reflect reality. This is why CAN takes into consideration three main aspects when building predictive models: Data, Theory, and Math. In our experience your predictive models will not reflect reality if all of three of these aspects are not held up. 

Tadd Wood

The Friendship Paradox

About 20 years ago, a sociologist named Scott Feld discovered an interesting phenomenon where on the average, people have less friends than their friends do.  However, most people believe they have more friends than their friends do.  This is the paradox.  The friendship paradox is a form of sampling bias.

Tadd Wood

How Much Data Do I Need For Predictive Analytics?

Before beginning any predictive analytics project, its essential to investigate the breadth and depth of data available. However, at what point is it acceptable to say you have enough data to start?

The politically correct answer to this question is that it depends. Depends on what though?

Well for starters, certain types of data science and predictive analysis projects require more specific data requirements. In an extreme case, predicting survival rates of people or machines may require data spanning their entire lifespan. However, in most cases, data requirements are less stringent.

In most cases taking a snapshot of 3 to 5 years worth of data can yield a breadth of patterns surrounding consumer and business behavior. Why?

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