Below are links to some of my projects using interpretable machine learning for my various interests. Predicting MLB Pitcher Strikeouts and Interpreting Predictions Video – link goes to my youtube channel
Forecasting EDA James Young Summary This notebook demonstrates an example exploratory data analaysis that would be conducted at the start of a forecasting project, that is, after the data has been gathered, cleaned, and verified to be accurate. First, we want to learn what it is we are forecasting. We want to learn the dimensions …
How To Make A Standalone Desktop App Using Shiny and Electron.
Data science is an umbrella term that can cover visualization, statistical inference, predictive modeling, machine learning, deep learning, artificial intelligence, data engineering, insert buzzword of the day here… (the list goes on). Regardless of what specific niche you’re trying to fit into in the data science universe, it’s likely you’ll rely on a few key languages. These popular languages include R and Python (and SQL when you have to). There are other languages out there that can be used for data analysis and machine learning, but R and Python are the most popular from what I’ve seen. I will discuss both of these primary languages below and weigh their strengths and weaknesses.