Moon Shot Genius

I am currently working on a recommendation engine (Moon Shot Genius (MSG)) for buy/sell on lower priced stocks that are likely targets of insider training. The inputs are various social media feeds and stock market data.

I am using Python, Pandas, SQLite, Tableau, and various supervised machine learning and deep learning algorithms.

This technology hooks into a trading engine that automatically provides stock data (price, volume, etc.), debates with MSG and then produces a buy, sell, or do nothing signal. A text message is then sent to the user with the appropriate message.

Prosper, P2P Lending Marketplace, Tableau

Savers using Commercial Deposits (CDs) (the most widely used savings vehicle in the USA) get terrible returns. Prosper returns are up to 20 times larger. This Tableau Story shows that, if you use Prosper you need to manage your losses. If you do, you achieve a +8% return over and above a 3-year CD return. Prosper is like the Chinese symbols for Crisis: Danger and Opportunity. There is opportunity but … you could easily lose your investment capital if you do not pay attention to managing loan losses.

“Prosper Marketplace, Inc. is a San Francisco, California-based company in the peer-to-peer lending industry. Prosper Funding LLC, one of its subsidiaries, operates, a website where individuals can either invest in personal loans or request to borrow money.”  Source Wikipedia. To see the Tableau Story click on the link below.

Prosper P2P Lending Marketplace 

Technologies utilized are Tableau, Python, and Pandas. If this interests you please contact me. This project was done as part of my course work at Udacity ( for the Data Analyst Nano Degree (DAND).

Stroop Effect, Descriptive Statistics

For the Jupyter Notebook that runs this project (HTML format) click this link:

The Stroop Effect

Technologies utilized are Python, Pandas, Matplotlib, and SciPy (statistics).

To make our lives simple, our brains are wired to simply respond to stimuli. We have learned over the years that most stimuli are congruent. When the wind blows the trees move. As a result, it is not necessary to measure the velocity of the wind. You can simply look out the window of the house and if the trees are moving, it is windy.

When we encounter stimuli that is incongruent, all this pre-set up wiring needs neutralizing for us to properly respond. It takes some time to process the stimuli and respond correctly since we need to fight thru all the pre-conditioning that we have developed over a life time. This difficulty is called the Stroop Effect.

The Stroop Effect is all around us. An example would be a police lineup. Supposedly the perp is in the line up. It is congruent that the perp is there. So, the witness picks one of the people in the lineup. Yet, all that may be occurring is the Stroop Effect. While this may sound academic, for the person that just got picked out of the lineup for a major crime, it is far from academic.

The net is for a wide range of stimuli, there is built in congruency bias. This may be helpful for us to run our lives, but … when true thinking is required, it is a large hurdle for us to a) recognize that is what is occurring and b) continue with the process to think rationally instead of reflexively.

If this interests you please contact me. This project was done as part of my course work at Udacity ( for the Data Analyst Nano Degree (DAND).


In September 2021 I completed my Data Scientist NanoDegree (DAND) from Udacity I have current skills in machine learning, deep learning, Natural Language Processing (NLP), computer vision, transfer learning, recommendation systems, A/B testing, Spark, and Flask. I can write and deploy machine learning applications from scratch to production and maintenance, including a variety of cloud offerings (AWS, Azure, and GCP).

During 2018, I received the Data Analyst Nano Degree (DAND) from Udacity. I learned how to extract, wrangle and organize data. I uncovered patterns and insights using advanced statistical analysis, drew meaningful conclusions, and clearly communicated critical findings. I learned Python, R, SQL (refreshed SQL, I used to work for Oracle) and Tableau.

When I graduated from U of C, I went to work for Honeywell Information Systems (HIS) as a pre sales support analyst helping them market mainframe computers. I was amazed at how much money HIS invested in training me! I intimately learned how computers worked from a hardware and software perspective and ended up learning 5 programming languages (Fortran, Cobol, Pl1, and 2 assembler languages), 3 database systems, and 2 operating systems.

I graduated from the University of Calgary, in Alberta, Canada in 1977 in business. I took more computing science courses than I needed to as electives. My 4 year GPA was 3.3 and my 4th year GPA was 3.7. I returned to U of C many years later and took a molecular biology high intensity course. We extracted, cut, and ligated a Green Fluorescent Protein (GFP) into e Coli. Pretty cool to manipulate nature to create a glowing organism! This was part of my training for my life sciences company (United Bioinformatica Inc.).