Customer Segmentation Dashboard

First go at creating a dashboard in Tableau. Data used was an example dataset of customer data from a newly formed bank in the UK. The data, and the project as a whole, was done originally as part of Udemy's Tableau A-to-Z course.

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Injured Worker Causes

Quick and dirty look at the causes of workplace injuries. Data obtained from and, while not a very comprehensive list (sample size is only 537), I'm using this as more of an experiment with Jupyter notebook and pelican.

Predicting Enron Fraud

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.

Predicting a Country's Happiness

A look at the world happiness index and an evaluation of the factors that contribute to the general happiness of a countries population. Variables were primarily focused on economic, political, and elegantarian factors. Originally conducted as a project for my SNHU Applied Stats II class. As it ended at around 30 pages, included is only a subset of the full project.

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Coffee Buyer Survey

Analysis of a quick three-day survey conducted on google forms regarding customer preferences and reasons for buying coffee beans. Not necessarily a randomized sample as it was distrbuted through social media means, yet still an interesting view on people's preferences when considering where to purchase coffee beans. Project originally conducted for a research methodologies class at Southern New Hampshire University.

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US Name Trends

Interactive D3.js visualization which allows for the input of several names and plots popularity of these names over time. Data obtained from 'US baby names' dataset. While this chat is interesting, it does not necessarily provide a deep look into the trends of names throughout the US and was primarily done for practice utilizing D3.js's interactive options. I also found I get an absurd amount of enjoyment in comparing name popularity after finishing this project.
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