#' Create random-forest training model for bicycle rental prediction.
#'
#' @param train_dataset Training dataset created by create_train_dataset()
#' @return random forest training model
#' @export
#' @importFrom randomForest randomForest
#' @examples
#' \dontrun{train_dataset <- create_train_dataset(3)
#' rf_model <- create_train_model(train_dataset)}
create_train_model <- function(train_dataset) {
check_data()
rf_model <- randomForest::randomForest(extract_features(train_dataset),
train_dataset$rentcount,
ntree = 50, mtry = 2, importance = TRUE)
return(rf_model)
}
#' Extract feature columns from train/test dataset
#'
#' @param data data with feature columns and others
#' @return data containing only feature columns
extract_features <- function(data){
features <- c ("hour", "month", "weekday", "season",
"Temperature", "Windspeed", "Humidity", "Rainfall")
return(data[, features])
}
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