Data Science

A Layman’s View of an Agile Environment

Agile Software Development has gained traction in recent years, expanding its foundational values to many sectors outside of tech. For the uninitiated, the plethora of technical jargon can be very intimidating, and those seeking to enter a technical role need to at least have an understanding of an Agile environment. What is Agile? Purists will… Continue reading A Layman’s View of an Agile Environment

Data Science

A Mathless Breakdown of the Random Forest Classification Algorithm

Review A previous post discussed how the decision tree algorithm classifies items using the gini impurity index--a simple score that is easily calculated. Please visit this post for some background information or a refresher if necessary. Pitfalls of Decision Trees Decision trees are relatively straightforward, regardless whether it is built on the gini impurity index… Continue reading A Mathless Breakdown of the Random Forest Classification Algorithm

Data Science

Fraud Classification with AutoML

Introduction Data scientist follow a structure of obtaining data, cleaning it, and then feeding it through one of their many fancy models to determine which one has the best "predictive" capabilities. This insight is highly valued by many companies either as a means to have some foresight into future conditions or revise current systems to… Continue reading Fraud Classification with AutoML

Data Science

A Mathless Breakdown of Decisions Trees and the Gini Impurity Index

The decision tree algorithm is one of the first learned by most aspiring data scientists because while the underlying math may be tedious, the overall logic is pretty simple to understand, at least on a surface level. At its core, a decision tree is a classifier algorithm, which means it has to make a decision… Continue reading A Mathless Breakdown of Decisions Trees and the Gini Impurity Index

Data Science

Evaluating Hospital Effectiveness: The First of Many Failures

  It's fine to celebrate success, but it is more important to heed the lessons of failure. -Bill Gates A Preface: To be blunt, I have failed. More accurately, my models have failed (by ironically being not accurate, all pun intended). Despite weeks of work, I have failed to provide anything conclusive about the objective… Continue reading Evaluating Hospital Effectiveness: The First of Many Failures