2019 TUN Data Challenge

Brief overview of the TUN Data Challenge I conducted with a team as part of SNHU experiential learning course. We work with client, marketing, and interaction data from non-profit, Hire Heroes USA to answer business problems specified by the organization. Our process involved: Defining goals, cleaning data, exploratory data analysis, statistical analysis, data visualization, and communicating results. Final results from the contest are still pending, expected in late July.

more ...

Identifying Advertisements with ANN's

I pull data from the UCI Machine Learning Repo and use it to train a model which can identify advertisements based upon their image size and URL terminology. I work through cleaning the data, attempting a few different fitting algorithms, and end with some parameter-tuning of an ANN. My final model results in over 97% accuracy in classifying advertisements in testing. Originally conducted for a Machine Learning course as SHHU focused on the R language.

more ...

Parallel Coordinates Plot using Plotly

Over the holiday season I heard several discussions on which charities are best to donate to and why some are better than others. With this in mind, I thought it would be interesting to examine the stats which set one charity over another and find a way to visualize these in an effective manner. With some help from Charity Navigator, I was able to source and collect the appropriate information and thought it a great time to finally give Plotly a go.

more ...

Multiple Linear Regression to Predict Consumer Spending

As in the last post, here's some more work in excel with economic variables. This time I use value forecasts of 30y mortgage, unemployment, and personal income rates, figured in a similar manner as before (annual growth/change rates - 10y moving averages) to predict future levels of personal consumption expenditures. I run a multilinear regression analysis to forecast PCE based upon the three independent variables and end up with some pretty strong results and an adjusted R-squared of .974.

more ...

Forecasting Economic Time-Series Variables in Excel

I grabbed some economic data from FRED and attempt to forecast future values for each variable using Excel. Econ variables of focus for this project include GDP, CPI, and the unemployment rate for the U.S. This was originally done for my Macro-econometrics class and provided me some nice time with Excel which I admittedly use too little.

more ...

Character Co-occurrence Network Diagram w/ NetworkX in Python

Being a big fan of fantasy novels, I've always had an interest in how characters within books with massive character lists all interweave and connect together. I've also had interest for awhile now in visualizing some type of complex network with networkX in Python. Oathbringer is the most recent book from Brandon Sanderson's Stormlight Archive series and it was a perfect option for me to combine both of these interests. The following is the code I used to parse through the etext of the novel and create a character co-occurence network diagram. Although certain decisions made throughout the process may not be perfect for representing direct 'co-occurrences', I found the resulting visual to be an interesting look at relationships seen throughout the book.