#' Feature Engineering PCA vector addition
#'
#' This function will take in data and add pca feature engineering (PC components) to the original table. For categorical variables,
#' MCA will be applied for feature reduction.
#' @import dplyr
#' @import data.table
#' @importFrom stats prcomp
#' @param a train data table
#' @param a test data table
#' @param ratio of variance explained (pca)
#' @param a number of components to be used (pca)
#' @param ratio of variace explained (mca)
#' @param a number of components to be used (mca)
#' @return a list with finished two data tables
#' @export
fe_pca <- function(train, test, pca_var, pca_comp, mca_var, mca_comp) {
# eraze zero variance and unique id
train <- as.data.table(fread("C://Users/taeha/Downloads/train.csv", stringsAsFactors = F))
test <- as.data.table(fread("C://Users/taeha/Downloads/test.csv", stringsAsFactors = F))
train_y <- train$SalePrice; train$SalePrice <- NULL
train <- preprocess_dt(train)
test <- slice_column(test, names(train))
# divide into categorical and numerical varaible
train_num <- get_num_data(train)
train_cat <- get_cat_data(train)
test_num <- get_num_data(test)
test_cat <- get_cat_data(test)
# do pca
md.pca <- prcomp(train_num, center = T, scale. = T)
# select variance
# predict to test
# add to varaible
# same for Mca
}
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