sample of using random forest to predict future :)

RandomForest

# read dataset from local file
data <- read.csv("/Users/kostya/Downloads/abalone.data.csv", header=F)

# set names for dataframe columns
colnames(data) <- c('Sex', 'Length', 'Diameter', 'Height', 'WholeWeight', 'ShuckedWeight',
                    'VisceraWeight', 'ShellWeight', 'Rings')

# split dataset into train and test seta
train.size <- floor(0.9 * nrow(data))
train <- data[1:train.size, ]
test <- data[(train.size+1):nrow(data), ]

# get the names of the columns
props <- names(data[,-length(names(data))])
props <- props[! props %in% 'Rings']
n <- length(props)

# construct all possible combinations
id <- unlist(
  lapply(1:n,
         function(i) combn(1:n,i,simplify=F)
  )
  ,recursive=F)

# and paste them to formula
Formulas <- sapply(id, function(i)
  paste("Rings~",paste(props[i],collapse="+"))
)

# evaluate all formulas
rf <- lapply(Formulas, function(i)
  randomForest(as.formula(i), data=train, ntree=70))

# pick up the formula based on the best prediction
bestRF <- rf[[1]]
bestRsq <- bestRF$rsq[ length(bestRF$rsq) ]
for(i in 2:length(rf)) {
  if( rf[[i]]$rsq[ length(rf[[i]]$rsq) ] > bestRsq ) {
    bestRF <- rf[[i]]
    bestRsq <- bestRF$rsq[ length(bestRF$rsq) ]
  }
}

# predict 
head( predict(bestRF, newdata=test) )