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

Please download the dataset file

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*
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