Data Analytics Workouts

While developing these demonstrations in logistic regression and neural networks, I used and discovered some interesting methods and techniques: Better Methods A few useful commands and packages...: update.packages() for updating installed packages in one easy action as.formula() for creating a formula that I can reuse and update in one action across all my code sections View() for looking at data frames fourfoldplot() for plotting confusion matrices neuralnet for developing neural networks caret , used with nnet , to create predictive model plotnet() in NeuralNetTools, for creating attractive neural network models Resources that I used or that I would like to explore... MS Azure Notebooks , for working online with Python, R, and F#, all part of MS's data workflows Efficient R Programming , that seems to have many good tips on working with R Data Mining Algorithms in SSAS, Excel, and R , showing various algorithms in each technology R Documentation , a ...

This is an example of neural networks using the neuralnet package on one of my sample data sets, walking through a Pluralsight training series, Data Mining Algorithms in SSAS, Excel, and R . In terms of results, the regression primarily predicts voter leanings based on five (5) traits, openness, conscientiousness, extraversion, agreeableness, and neuroticism, although only the first two (2) traits have a significant impact. Logistic regression is about 85% predictive, and slightly better at predicting Red states over its ability in predicting Blue states. [1] "Logistic Regression (All) - Correct (%) = 0.854166666666667" [1] "Logistic Regression (Red) - Correct (%) = 0.892857142857143" [1] "Logistic Regression (Blue) - Correct (%) = 0.8" For the neuralnet package, I created a loop to vary the hidden layers and the number of repetitions, since this is such a small number of records. This is obviously much less predictive than logistic regressi...