In a recent NLP project, I took a data set from Kaggle and investigated it from several different angles. In the process, I implemented four different modeling techniques, each for a different purpose. I plan to shed a light on each technique and show how one project can be split up into several mini projects, each with their own goal, to achieve results.

The Data

5 million podcast reviews with both written and numerical ratings. The data is updated monthly and organized very well. The only feature I would request added is a User ID for the consumers leaving the reviews. …

What is SQL, and Why do I Need it?

SQL is a query language designed primarily to access and manage data stored in a relational database. There are many Dialects of SQL available and the one you end up using will likely be dictated by either your employer or personal preference. These Dialects are much like the languages we speak: they may use differing terms for very specific reasons, but are built on the same foundation of communication. This blog is to illuminate some of these foundational methods that every Dialect uses.

Examples of different dialects

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The single most important distinction to learn is the difference between a table and a dataset. A…

The Project

Data science projects can often be described as using 15 pounds of potatoes to make one plate of french fries. Creating a model is a process of iteration, of constant creation and retooling until you have the ideal combinations and parameters to meet your goal. The work of model creation is very front-loaded. From exploring your data to creating features to creating and analyzing models, it can seem to be a lot of work to create one specific model.

All those potatoes, one plate of fries

My recent project used Natural Language Processing to analyze Tweets and create a model to predict a positive or negative sentiment…

Images taken from
All coding done using Python and Python’s statsmodels library.

Let’s Figure Out What This Means!

Don’t be intimidated by the big words and the numbers! This blog is here to translate all that information into plain English. Our goal is to provide a general overview of all statistics. Further research is highly recommended for in depth analysis for each component.
Let’s start at the beginning.

Tim McAleer

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