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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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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Demographic Analysis

Posted by Christopher Mertin on December 02, 2016 in Visualization • 1 min read

Visualization using D3.js of US Demographic Data using a Na├»ve Bayes approach.


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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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Linear Regression

Posted by Christopher Mertin on November 05, 2016 in Imputation • 4 min read

Explores the use of linear regression.


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