Voting Ensemble in Spam Classification - NLP research focusing on identify spam in SMS data. I develop a voting ensemble model that considers 11 models for membership including - Random Forests, BERT, Extremely Randomized Trees, Gradient Boosted Trees, Random Forest with Extreme Gradient Boosting, Naïve Bayes, Support Vector Machines, Convolutional Neural Network, Long Term Short Term Memory network, Adaptive Boosting, and Logistic Regression.

   Company Bankruptcy Prediction - Application of various classification techniques to predict company bankruptcy. Methods evaluated include Logistic Regression, SVC, Naïve Bayes, Random Forests, Gradient Boosted Trees, and Extra Trees.

   CNN to detect Digits - Machine vision project that applies a convoluted neural network to identify handwritten numbers.

   Unsupervised Learning to detect Digits - As part of the same Kaggle competition, this project applies unsupervised learning techniques (K-mean and PCA) to indentify images of handwritten numbers.

   Discrete Event Simulation - Discrete event simulation applied to a call center. Model allows for phone call handoffs, balking, and routing.

   SQL Portfolio - Collection of SQL queries that build data outputs relevant to actuaries. Queries included for building both left and justified triangles and counting claims.