library(glmnet)
library(varSelRF)
library(FSelector)
library(mlbench)
library(plot3D)
library(knitr)
library(rgl)
knit_hooks$set(webgl = hook_webgl)

x <- sort(rnorm(1000))
y <- rnorm(1000)
z <- rnorm(1000) + atan2(x,y)
plot3d(x, y, z, col=rainbow(1000))

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")
plot3d(x, y, z, col=rainbow(1000))

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

features <- 3:45 
complete <- which(rowSums(is.na(dat[, features]))==0)

datc <- dat[complete, ]
x <- as.matrix(datc[, features])
y <- datc[,2]
cl <- factor(y)

#column one contains the names of the datapoints (i.e cereal xxx)
nascar_data_with_label=datc[,(-1)]
selected_features<-c()