Andrew Trick

Being a big fan of fantasy novels, I've always had an interest in how characters within books with massive character lists all interweave and connect together. I've also had interest for awhile now in visualizing some type of complex network with networkX in Python. Oathbringer is the most recent book from Brandon Sanderson's Stormlight Archive series and it was a perfect option for me to combine both of these interests. The following is the code I used to parse through the etext of the novel and create a character co-occurence network diagram. Although certain decisions made throughout the process may not be perfect for representing direct 'co-occurrences', I found the resulting visual to be an interesting look at relationships seen throughout the book.

I've wanted to get more practice with natural language processing, so I grabbed a dataset of Wikipedia comments from a past Kaggle challenge to attempt to classify toxicity of each comment. Train data was a collection of over 150k comments connected to user-defined classifications of 'toxic', 'severe toxic', 'obscene', 'insult', 'threat', 'hate'. Here's the process I took to create a model which identifies these particular classifications of future comments!

First is to import libraries we'll be using

Summary: I recently tracked my daily coffee consumption and thought it would be interesting to find a fun way to visual it. After some exploration of options in R, I decided to give a Nightingale Rose Diagram a try.

Summary: It's been alittle over three years since I attempted my first ML project of classifying survival of the titanic.. A pretty famous dataset and task for this field I'd say. I achieved a accuracy of around 75% in my first attempt at this (I believe with a random forest model). I though it would be nice to revisit this and give it another go now that I'm a little more experienced. Additionally, I've wanted to explore ensemble learned some more, particularly XGBoost. Let's dive in!