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: 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!

Part three of my attempt to predict NCAA tourney results based on past game data. As cleaning and var creation is out of the way now, this segment will focus on fitting the data to different classifiers in scikit-lelarn and tweaking parameters to determine a classification model with the best prediction power.

Testing and evaluating numerous machine learning techniques to determine best option for predicting fruad occurances in Enron email dataset. The most efficient predictor ended up being an Adaboost algorithm with 50 n_estimators. This method using decision tree as a 'weak learner' came out with about 85% accuracy, p-value of 39, and an r-squared of around 32. Originally conducted for Udacity Nanodegree project.