**Analytics , Education , Campus and beyond**, and kindly contributed to R-bloggers)

Classification is a supervised task , where we need preclassified data and then on new data , I can predict.

Generally we holdout a % from the data available for testing and we call them training and testing data respectively. So it’s like this , if we know which emails are spam , then only using classification we can predict the emails as spam.

I used the dataset http://archive.ics.uci.edu/ml/datasets/seeds# . The data set has 7 real valued attributes and 1 for predicting . http://www.jeffheaton.com/2013/06/basic-classification-in-r-neural-networks-and-support-vector-machines/ has influenced many of the writing , probably I am making it more obvious.

The library to be used is library(nnet) , below are the list of commands for your reference

seedstrain<- sample(1:210,147)

seedstest <- setdiff(1:210,seedstrain)

ideal <- class.ind(seeds$Class)

seedsANN = nnet(irisdata[seedstrain,-8], ideal[seedstrain,], size=10, softmax=TRUE)

predict(seedsANN, seeds[seedstrain,-8], type="class")

table(predict(seedsANN, seeds[seedstest,-8], type="class"),seeds[seedstest,]$Class)

Happy Coding !

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