Data Analytics Workouts

This is an example of Support Vector Machines, using one of my usual data sets, as part of a Pluralsight training presentation, Data Mining Algorithms in SSAS, Excel, and R . In terms of results, the prediction primarily predicts voter leanings based on two (2) traits, openness and conscientiousness, and although using all five (5) factors improved the prediction quality, plotting that is problematic. For this, the model is 96% predictive of Republican outcomes, but only 66% accurate in predicting Democratic leaning. Politics.prediction Blue Red Blue 15 1 Red 5 27 The code is below, as are some related graphs. Source data is here . # Clear memory rm(list = ls()) # set Working directory getwd() setwd('../Data') # load data Politics.df <- read.csv("BigFiveScoresByState.csv", na.strings = c("", "NA")) # clean data - remove NULLs Politics.df <- na.omit(Politic...

This was a little bit of fun, after reading a few more chapters of Text Analysis with R for Students of Literature . Spinoza is a current interest, as I am also reading Radical Enlightenment: Philosophy and the Making of Modernity 1650-1750 . Example Code (Common to Subsections) # Text for this can be acquired as below Project Gutenberg, as below # http://www.gutenberg.org/cache/epub/989/pg989.txt, # or via a Sample Data at the end of this post: textToRead = 'pg989.txt' # Text for this can be acquired via Matthew Jockers site, as below, # http://www.matthewjockers.net/macroanalysisbook/expanded-stopwords-list/, # or via a Sample Data at the end of this post: exclusionFile = 'StopList_Extended.csv' # Read Text text.scan <- scan(file = textToRead, what = 'char') text.scan <- tolower(text.scan) # Create list text.list <- strsplit(text.scan, '\\W+', perl = TRUE) text.vector <- unlist(text.list) # Create ...