Enhancing model results with an advanced imputation strategy


While having a nice, clean dataset with minimal preprocessing needs is the ideal scenario for many data scientists, real world data is typically anything but ideal. Certain preprocessing steps such as normalization and transformation aid in creating the best model possible, but are technically optional — that is, a model…

An overview of commonly used tools and metrics


Classification models represent some of the most useful and practical algorithms in the machine learning world. From predicting whether it will rain to determining fraudulent activity on credit cards, these types of models use the available data given to them to classify their predicted outputs into two or more groups…

An intuitive visualization of why we use t-distributions


In this article I attempt to provide an intuitive visualization of why Student’s t-distribution is often used over the normal distribution. Many introductory statistics and data science courses provide a rationale for the use of t-distributions along the lines of it being useful in situations where either the sample size…

T.J. Kyner

Software Engineer at Rhove. Former Investment Specialist for a private wealth management firm.

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