library(readr)
library(plyr)
library(dplyr)


one_training_data<-read.csv('../input/train.csv', header=T, nrows=1)
names(one_training_data)
classes <- sapply(one_training_data, class)
big_training_data<-read.csv('../input/train.csv', header=T,colClasses = classes,nrows=100000)
str(big_training_data)


one_testing_data<-read.csv('../input/test.csv', header=T, nrows=1)
head(one_testing_data)
classes1 <- sapply(one_testing_data, class)
big_testing_data<-read.csv('../input/test.csv', header=T,colClasses = classes1,nrows=1000000000)
str(big_testing_data)


 
model <- lm(Demanda_uni_equil ~ Semana +Agencia_ID+Canal_ID+Ruta_SAK+Cliente_ID+Producto_ID,data=big_training_data, )
summary(model)
predicting<-predict(model,big_testing_data)
str(predicting)


submit <- data.frame(ID=big_testing_data$id, Demanda_uni_equil=predicting)
names(submit)
which(submit$Demanda_uni_equil<0)
submit$Demanda_uni_equil[submit$Demanda_uni_equil < 0] <- 1
write.csv(submit, "submit.csv", row.names = F)