Neural Network (Machine Learning) .. when Data doesn't respond well, add Features

The Problem posted previously neural not respond I tried to find the problem based on actual data-

Please download the dataset file 

DataSet

Now First I tried to run the logic on existing set of data -

 library("neuralnet")  
 setClass("myDate")  
 data <- read.csv("D:/tmp/mlclass-ex1-005/mlclass-ex3-005/R-Studio/account.csv")  
 head(data)  
 #Replace the comma from Amount  
 data$Amount <- as.numeric(gsub(",", "", gsub("", "", data$Amount)))  
 #Change Dr.(1) and Cr.(0)  
 data$Transaction <- as.numeric(data$Transaction=="Dr.")  
 #Split Date in Day Month & year  
 data$Transaction.Date <- as.Date(data$Transaction.Date, format="%d/%m/%Y")  
 month = as.numeric(format(data$Transaction.Date, format = "%m"))  
 year = as.numeric(format(data$Transaction.Date, format = "%Y"))  
 head(data)  

And the data looks like-

For this data I already provided different plots based on different perm comp here Previous

And as we know we get a huge Error rate after running the neural network algorithm -

 output <- neuralnet(Transaction ~ Amount+month,data,hidden = 4,threshold = 0.01,linear.output=FALSE, likelihood=TRUE)  
 print(output$result)  
 plot(output,rep = "best")  

I am going to present an another way of visualizing the result.
Visualize the result from using generalized Weights
gwplot uses  the calculated generalized weight provided by nn$generalized.weights

 out <- cbind(output$covariate,output$net.result[[1]])  
  dimnames(out) <- list(NULL, c("Amount","Month","nn-output"))  
  head(out)  

 #Plotting Generalized weight  
 #The distribution of the generalized weights suggests that the covariate Amount  
 #has no effect on the case-control status since all generalized weights are nearly zero  
 par(mfrow=c(2,2))  
 gwplot(output,selected.covariate="Amount", min=-2.5, max=5)  
 gwplot(output,selected.covariate="month", min=-2.5, max=5)  

The distribution of the generalized weights suggests that the covariate Amount has no effect on the case-control status since all generalized weights are nearly zero

I Added few features in my Data Set with some sense and again did all these steps 

 library("neuralnet")  
 data_new <- data;  
 data_new[c("A","B","C")] <- NA  
 data_new$A <- sample(1:10,nrow(data_new),replace = TRUE)  
 data_new$B <- sample(22:30,nrow(data_new),replace = TRUE)  
 data_new$C <- as.numeric(data_new$Transaction =="1")  
 head(data_new)  

And after running rest of the code,

 plot(data_new$Amount, data_new$A+data_new$B+data_new$C, main="Transaction vs Amount",   
    xlab="Amount", ylab="A+B+C", pch=1, col="red")  
 output_new <- neuralnet(Transaction ~ Amount+A+B+C,data_new,hidden = 4,threshold = 0.01,linear.output=FALSE, likelihood=TRUE)  
 print(output_new$result)  
 plot(output_new,rep = "best")  
 #How well my Data Fits Here  
 out_new <- cbind(output_new$covariate,output_new$net.result[[1]])  
 dimnames(out_new) <- list(NULL, c("Amount","A","B","C","neural-output"))  
 head(out_new)  
 par(mfrow=c(2,2))  
 #two covariates A and C have a nonlinear effect since   
 #the variance of their generalized weights is overall greater than one  
 gwplot(output_new,selected.covariate="Amount", min=-2.5, max=5)  
 gwplot(output_new,selected.covariate="A", min=-2.5, max=5)  
 gwplot(output_new,selected.covariate="B", min=-2.5, max=5)  
 gwplot(output_new,selected.covariate="C", min=-2.5, max=5)  

I got an excellent Error rate as well as distribution of the generalized weights were good

























two covariates A and C have a nonlinear effect since  the variance of their generalized weights is overall greater than one