User Interface Design & Development:
SQL/NoSQL Database Design & Integration / REST API
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Docker & Other DevOps:
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Python ➡ DataSci & WebApp Frameworks
Customized Application Integration / ETL / Data Pipelines
Here's an introduction to one of my favorite projects.
It's a predictive model, in an R script, sitting on my server.
When you click this button, it samples a set of data (crime in 36 zip codes in Austin, TX) and generates a trained model with ~83% of the data (30/36 data points)-- visualized as a predicted confidence interval (upper limit, lower limit, and average prediction), to which test data is applied (the remaining 17% of the data (6 data points)) to see how accurately the model (which is randomly calibrated— "trained" with a randomly selected 83% of the crime-correlated data— when you click) predicts real data.