SHAP or Shapley Additive Explanations helps you understand ML models.
The SHAP framework unifies the methods used to interpret and explain machine learning models. This post helps interpret and explain SHAP. Read this post to start getting into SHAP (with both high-level explanation and python example).
Time series analysis is a powerful technique to generate forecasts with seasonal data.
Data sets contain noise but with high-powered or elegant data science, the relevant signal can be extracted. One key technique for analysis of real-world data (primarily focused on forecasting) is time-series analysis. A popular time-series forecasting procedure is Facebook's open-source Prophet procedure. Prophet is implemented in both R and Python.
An expression of gratitude and summary of experience from one of Carnegie Mellon's latest graduates.
Carnegie Mellon University's Heinz College offered a unique opportunity to jointly study public policy, management, and data analytics. The coursework covers topics ranging from machine learning, deep learning, econometrics, and optimization to organizational design and decision-making. Continue reading to learn why I am glad I enrolled.
Using only ingredients, learn how to classify recipes into cuisines using Python, classification methods, clustering methods, and Sci-kit Learn.
While seemingly a trivial task, classifying recipes into cuisines and understanding how to interpret clustering and classification results can help you creatively answer other questions. Continue reading to learn how.
I use machine learning and data science to identify patterns and relationships in data.
I earned my Master's in Public Policy & Data Analytics from Carnegie Mellon in 2019 and my Bachelor's in Information Sciences & Technology from the Pennsylvania State University in 2013. All views on my website are my own and do not represent the organizations I work with unless clearly stated.
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