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Scratch Models adding SVM model froms scratch Dec 5, 2018
docs updating projects Nov 15, 2018
figures updating log reg Dec 3, 2018 updating log reg Dec 3, 2018

Data Science Portfolio/Projects

Statistical Models from Scratch

I find the best way to learn a specific algorithm or statistical model is to build one from scratch. The following files are classes and functions that accomplish the most common statistical learning methods on a limited level.

  • Keywords(R, Python, Statistical Modeling, Algorithms)

Fine Scale Weather Data from 1900-2013

  • Builds daily gridded weather data for the continental United States from 1900-2013.

  • Relative anomaly spline interpolation technique calculates daily weather data for 460,000 2.5km x 2.5km grids in the US. [Tech. Example]

  • Aggregates down to county level weather data.

  • Keywords(R, Economics, Climate Change, Weather)

Nonlinear Temperature Distributions [R package] [Python Package]

  • Calcuate nonlinear temperature distributions degree days and time in each degree.

  • Measure accounts for the rise and fall of temperatures during the day.

  • Degree days define time above a specified temperature threshold (e.g. degree days above 30C) and time in each degree define time within a specified temperature threshold (e.g. time in 30C).

  • Keywords(R, Python, Economics, Climate Change, Agronomy)

Business Case: Wine Quality and Price

  • Predict wine quality based on biophysical characteristics.

  • Model using Multinomial logit, Linear Discriminant Analysis, Random Forest, and Extreme Gradient Boosting

  • Keywords(R, Classification, Economics)