Bank Marketing Strategy

Posted by Christopher Mertin on August 21, 2017 in Project • 13 min read

Using statistics, deep learning, SVMs, and Gaussian Mixture Models on customer banking information to build a predictive model to optimize who the bank markets term deposits to so that marketing resources are not wasted on those unlikely to enroll.


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Rossmann Store Sales Analysis & Forecasting

Posted by Christopher Mertin on July 24, 2017 in Project • 16 min read

Uses public data from Rossmann stores with feature creation to analyze store sales and create a business forecast. Built models to predict sales at certain stores for a given day, as well as found the most important factors driving sales.


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Searching for Exotic Particles in High Energy Physics

Posted by Christopher Mertin on June 09, 2017 in Project • 8 min read

Using data pertaining to Super Symmetry (susy), I explore the use of various algorithms to try and identify particles from data. I look at the use of a Random Forest, Gaussian Na├»ve Bayes, Multi-Layer Perceptron, and Deep Learning with the use of Dropout.


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Efficient Deep Neural Networks

Posted by Christopher Mertin on May 05, 2017 in Project • 2 min read

Masters project at the Univeristy of Utah which explores the use of Hierarchical Matrices to increase the learning rate and computation speed of Deep Neural Networks.


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Unsupervised Learning of Religious Facial Features

Posted by Christopher Mertin on April 29, 2017 in Project • 1 min read

Explores the use of eigenfaces and clustering algorithms to differentiate people with religious heritage with up to 80% accuracy.


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Movie Recommendations (Part 2)

Posted by Christopher Mertin on February 19, 2017 in Project • 13 min read

An extension of the previous movie recommendation system, however with the use of Latent Factors with Keras to create a Neural Network to calculate the similarities instead of using MapReduce. This is a much more accurate implementation as it allows the addition of a bias term.


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Movie Recommendations (Part 1)

Posted by Christopher Mertin on January 15, 2017 in Project • 8 min read

With the use of the MapReduce ecosystem, and user movie ratings from the movie lens data set, I build a system using the cosine distance between user movie ratings to find movies that are similar to each other.


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Predicting Titanic Survival Rates

Posted by Christopher Mertin on December 22, 2016 in Project • 5 min read

Uses data from the Titanic to predict if someone would survive or not on the titanic based on various characteristics.


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Credit Card Fraud

Posted by Christopher Mertin on November 17, 2016 in Project • 9 min read

Using Machine Learning to look at credit card transactions to predict which are fraudulent. Contains unbalanced data as the number of true fraudulent transactions is less than 1%.


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Predicting Air Quality

Posted by Christopher Mertin on November 11, 2016 in Project • 7 min read

Uses linear regression to predict the air quality for a given day, and utilizes KNN Imputation for missing data, as well as the creation of features.


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