> str(DATABASE)
'data.frame':   1667 obs. of  28 variables:
 $ YEAR_SALES            : num  2 1 2 2 1 1 1 1 1 1 ...
 $ MONTH_SALES           : num  9 9 2 9 3 3 11 12 3 6 ...
 $ DAY_SALES             : num  13 3 10 23 12 10 26 4 18 9 ...
 $ HOURS_INS             : num  17 14 18 16 23 18 16 12 17 16 ...
 $ CREATION_YEAR_SALES   : num  2 1 2 2 2 1 1 2 1 1 ...
 $ CREATION_MONTH_SALES  : num  9 9 2 10 12 3 11 2 3 6 ...
 $ CREATION_DAY_SALES    : num  13 11 15 31 5 10 27 7 18 9 ...
 $ VALIDATION_YEAR_SALES : num  2 1 2 2 2 1 1 2 1 1 ...
 $ VALIDATION_MONTH_SALES: num  9 9 2 11 12 3 12 2 3 6 ...
 $ VALIDATION_DAY_SALES  : num  15 14 16 3 6 19 1 8 21 10 ...
 $ AGE_CUSTUMER          : num  32 37 23 32 44 33 29 30 56 48 ...
 $ MEAN_Sales            : num  0 71 50 0 0 83 0 25 23 35 ...
 $ NBR_GIFTS             : num  1 1 1 1 1 1 1 1 4 3 ...
 $ TYPE_PEAU             : num  2 3 4 2 2 3 2 2 2 2 ...
 $ SENSIBILITE           : num  3 3 3 2 1 3 3 2 2 2 ...
 $ IMPERFECTIONS         : num  2 3 2 1 3 2 2 1 2 1 ...
 $ BRILLANCE             : num  3 1 1 3 3 3 3 3 3 3 ...
 $ GRAIN_PEAU            : num  3 3 3 3 1 3 1 1 1 3 ...
 $ RIDES_VISAGE          : num  1 1 1 3 3 3 3 1 3 1 ...
 $ ALLERGIES             : num  1 1 1 1 1 1 1 1 1 1 ...
 $ MAINS                 : num  2 3 3 3 2 2 2 2 2 2 ...
 $ PEAU_CORPS            : num  1 2 2 1 1 1 1 1 1 1 ...
 $ INTERET_ALIM_NATURELLE: num  1 3 3 1 3 1 1 1 3 1 ...
 $ INTERET_ORIGINE_GEO   : num  1 2 1 1 3 1 3 1 1 3 ...
 $ INTERET_VACANCES      : num  2 3 1 2 1 2 1 1 2 3 ...
 $ INTERET_ENVIRONNEMENT : num  1 3 3 3 3 1 1 1 1 1 ...
 $ INTERET_COMPOSITION   : num  1 1 1 3 3 1 1 1 1 1 ...
 $ OUTCOME               : num  3 4 7 3 3 6 3 9 26 17 ...

> set.seed(123)
> smp_size <- floor(0.75 * nrow(DATABASE))
> train_ind <- sample(seq_len(nrow(DATABASE)),size =smp_size)
>
> train <- DATABASE[train_ind, ]
> test <- DATABASE[-train_ind, ]
> reg<-lm(OUTCOME~.-1,data=train)

> y.test<-test$OUTCOME
> NBR_Achat=predict(reg,newdata=test)
> round(sqrt(mean(((1-NBR_Achat/y.test)^2))),4)
[1] 0.4523

y<-train$OUTCOME
x<-as.matrix(train[,1:27])
lambdas <- 10^seq(3,-2,by=-.1)
fit<-glmnet(x,y,alpha =0,lambda=lambdas)

> cv_fit <- cv.glmnet(x,y,alpha = 0,lambda=lambdas)
> plot(cv_fit)
> opt_lambda <- cv_fit$lambda.min
> opt_lambda
[1] 0.1

> x<-as.matrix(test[,1:27])
> y_predicted <- predict(cv_fit,s = opt_lambda,newx=x)
> y.test<-test$OUTCOME
> round(sqrt(mean(((1-y_predicted/y.test)^2))),4)
[1] 0.4605