NASCAR Data

library(glmnet)
library(varSelRF)
library(FSelector)
library(mlbench)
library(knitr)
library(plot3D)
library(rgl)
library(R.basic)
set.seed(2)

We chose to have 3 categories.
A Gain in rank -> Black – Improves rank
No Change in rank -> Red
A Loss in rank -> Green – Falls behind
Load the data

dat <- read.csv("C:/Users/Prashan/Dropbox (MIT)/MIT/Predictive Analytics/code/data/NASCAR_5f/allf_3c_phoenix2_2014_prototype_sel.csv")

Start with 43 features, create the x-attributes, y-label matrices

dat <- read.csv("C:/Users/Prashan/Dropbox (MIT)/MIT/Predictive Analytics/code/data/NASCAR_5f/fold1/train/fold_1_train.csv")

plot3d(dat[1:5,3],dat[1:5,4],dat[1:5,5])

## Warning in persp.default(x = xdummy, y = ydummy, z = zdummy, xlim = xlim, :
## surface extends beyond the box