Shad Griffin – Medium

Published in Geek Culture

·Jan 8

Machine Learning with Biased Data

Moving Forward when the Historical Record Includes Racial, Gender or Other Types of Discrimination — AI has the power to save the world or destroy it. As Data Scientists, we must ensure that the AI we create first does no harm.medium.com ml_bias/Bias final version.ipynb at main · shadgriffin/ml_bias Contribute to shadgriffin/ml_bias development by creating an account on GitHub.github.com A few years ago, I heard someone talking about bias in machine learning models. My initial response? “What the heck is bias in machine learning models?” I seriously had no idea. Of course, there are different types of bias when doing Data Science, for example, confirmation bias, but I had…

Data Science

17 min read

Published in Geek Culture

·Nov 24, 2021

AI has the power to save the world or destroy it.

As Data Scientists, we must ensure that the AI we create first does no harm. — Artificial Intelligence impacts modern life every day. Applying for a loan, contacting your utility company, or driving a newer model car means interacting with AI. Each day AI transforms the way we live and interact with each other. As data scientists, we create AI. The power to develop AI comes…

7 min read

AI has the power to save the world or destroy it.

Published in Geek Culture

·Apr 7, 2021

Feature Engineering for Equipment Failure and Predictive Maintenance Problems

Transforming your data for better results — shadgriffin/feature_engineering_equipment_failure Contribute to shadgriffin/feature_engineering_equipment_failure development by creating an account on GitHub.github.co For more information on the topic, please see the following article. Machine Learning for Equipment Failure Prediction and Predictive Maintenance (PM) I spent roughly four years of my life studying equipment failure problems as a Data Scientist. This article includes…medium.com

Equipment Failure

20 min read

Published in The Shadow

·Feb 10, 2021

A Theoretical and Practical Review of Elasticity and Pricing Strategy

Understand the basics …. maximize profits — shadgriffin/Pricing_Tutorial a series of three notebooks that explore Smarter Pricing with IBM’s Watson Studio Introduction I finally bit the bullet…github.c Pricing is a complex topic, and every business in the world has to deal with it. Whether you are a nine-year-old with a lemonade stand or a transnational corporation, price is the mechanism that you use to operate your business. You provide a good or service. …

24 min read

A Theoretical and Practical Review of Elasticity and Pricing Strategy

Published in Towards Data Science

·Jan 13, 2021

Summarizing Insights/Predictions from Multiple Machine Learning Models into a Complete view of a Customer, Prospect or other Entity

Leveraging multiple insights to make real world recommendations — shadgriffin/profile_of_1 Contribute to shadgriffin/profile_of_1 development by creating an account on GitHub.github.com Intro Data Science, at its core, is about understanding entities. What’s an entity? Well, an entity is the “thing” that you are trying to understand. For example, an entity can be a store, a customer, a machine, or an employee. …

Small Businees

13 min read

Summarizing Insights/Predictions from Multiple Machine Learning Models into a Complete view of a…

Published in Towards Data Science

·Oct 2, 2020

Assigning Panel Data to Training, Testing and Validation Groups for Machine Learning Models

Make sure your groups are independent and avoid deployment disasters shadgriffin/panelsampling Contribute to shadgriffin/panelsampling development by creating an account on GitHub.github.com Cross-Sectional data includes individual entities measured in one time period. For example, 10,000 people measured once is cross-sectional data. Time series includes one entity measured over multiple time periods. …

6 min read

Assigning Panel Data to Training, Testing and Validation Groups for Machine Learning Models

Published in Towards Data Science

·Sep 4, 2020

Leveraging Value from Postal Codes, NAICS Codes, Area Codes and Other Funky-Arse Categorical Variables in Machine Learning Models

A value is worthless unless it tells you something. — shadgriffin/zip_code Permalink Dismiss GitHub is home to over 50 million developers working together to host and review code, manage…github.com The zip (postal) code you live in says a tremendous amount about you. (At least in North America). Where you live suggests: your annual income, whether you have children, the TV shows you watch and your political leanings.

7 min read

Leveraging Value from Postal Codes, NAICS Codes, Area Codes and Other Funky-Arse Categorical…

Published in The Startup

·Aug 4, 2020

Converting a Probability to Fail Into a Time to Failure Metric

Getting the most out of your Probability to Fail Models — shadgriffin/phat_to_ttf Contribute to shadgriffin/phat_to_ttf development by creating an account on GitHub.github.com With predictive maintenance problems, there are two common metrics that represent the health of your asset. The first is a probability to fail. That is, at a given moment in time, what is the probability that your machine will fail. Sometimes, this is represented by a health score. …

Probability To Fail

9 min read

Published in The Startup

·Jul 24, 2020

Determining a Cut-Off or Threshold When Working With a Binary Dependent (Target) Variable.

Where is the best place to draw the line? shadgriffin/best_cut_off Contribute to shadgriffin/best_cut_off development by creating an account on GitHub.github.com When predicting a binary dependent variable, the output of your model is usually a probability or is easily converted to a probability. Many times it is desirable to convert this probability to a binary variable to match the dependent variable.

Data Science

13 min read

Determining a Cut-Off or Threshold When Working With a Binary Dependent (Target) Variable.

Published in The Startup

·Jul 24, 2020

Machine Learning for Equipment Failure Prediction and Predictive Maintenance (PM)

I spent roughly four years of my life studying equipment failure problems as a Data Scientist. This article includes the better part of what I learned along the way. shadgriffin/machine_failure Contribute to shadgriffin/machine_failure development by creating an account on GitHub.github.com Originally published in July 2020. Significantly revised February 2021. In this notebook, I walk through a predictive maintenance problem in…

Predictive Maintenance

27 min read