Introdução

O programa possui o objetivo de executar metodos de redes neurais para regressao utilizando a base de dados House Prices presente no link: https://www.kaggle.com/c/house-prices-advanced-regression-techniques.

O código

suppressMessages(suppressWarnings(library(tidyverse)))
library(keras)
model <- keras::load_model_hdf5(filepath = "modelo_rnn")

mod_boost <- xgboost::xgb.load(modelfile = "mod_boost1")

Iniciando carregando os pacotes que serao utilizados como artificios para o codigo.

train <- read.csv("/opt/datasets/houseprices/train.csv",sep = ",",
                  dec = ".")
train <- readr::read_csv("/opt/datasets/houseprices/train.csv",na = "character")
test <- read.csv("/opt/datasets/houseprices/test.csv",sep = ",",dec = ".")
submission <- read.csv("/opt/datasets/houseprices/sample_submission.csv",sep = ",",dec = ".")
train %>% 
  select_at(.vars = vars(ends_with("Area"))) %>% 
  select_if(is.numeric) %>% 
  gather(var,value) %>% 
      ggplot(aes(x = value)) +
    geom_histogram(bins = 25,
                 fill = "#2b8cbe",
                 colour = "black") +
    theme_light() + 
    facet_wrap(~var, scales = "free")

Parte do codigo one é executado os graficos relacionados a area, e logo depois a relaçao das variaveis com venda.

train %>% 
  select_at(.vars = vars(ends_with("Sold"))) %>% 
  select_if(is.numeric) %>% 
  gather(var,value) %>% 
  ggplot(aes(x = value)) +
  geom_histogram(bins = 10,
                 fill = "#2b8cbe",
                 colour = "black") +
  theme_light() + 
  facet_wrap(~var, scales = "free")