I wanted to try out some of what I’ve learned with python for data science. I thought: Why not try it on the Kaggle Titanic challenge?
I was inspired to participate in Kaggle’s 2019 Data Science Bowl. In this post I link to some of my exploratory analysis and predictive models.
I made an algorithmic trader in R based on the “moving average crossover” technical indicator and performed a backtest using the TSLA stock. It did not perform well, but it was an interesting challenge that involved some timeseries analysis.
Using body measurement data from the National Health and Nutrition Examination Survey (NHANES), I created a model that predicts Gildan t-shirt sizes from height and weight.
A mini-tutorial on web scraping with R.
I wrote an R package for the Nutritionix API to do nutrition analysis on foods and recipes.
An analysis of the data science job market from scraped LinkedIn data
A tutorial on using pyMTurkR to create a HIT with a Qualification Test.
I wrote an R package for the MTurk Requester API because MTurkR was being deprecated. I call it “pyMTurkR” because it uses R reticulate to interact with the python boto3 library for API access.
A mini research study on what MTUrk workers consider to be fair compensation. I have used MTurk a lot in the past, so I wanted to know if what I was paying was fair.