Add files via upload

Author: vamsiry

NN.R

@@ -0,0 +1,60 @@
+#install.packages('ISLR')
+library(ISLR)
+
+print(head(College,2))
+
+#Data Preprocessing
+# Create Vector of Column Max and Min Values
+maxs <- apply(College[,2:18], 2, max)
+mins <- apply(College[,2:18], 2, min)
+
+# Use scale() and convert the resulting matrix to a data frame
+scaled.data <- as.data.frame(scale(College[,2:18],center = mins, scale = maxs - mins))
+
+# Check out results
+print(head(scaled.data,2))
+
+
+#Train and Test Split
+# Convert Private column from Yes/No to 1/0
+Private = as.numeric(College$Private)-1
+data = cbind(Private,scaled.data)
+
+library(caTools)
+set.seed(101)
+
+# Create Split (any column is fine)
+split = sample.split(data$Private, SplitRatio = 0.70)
+
+# Split based off of split Boolean Vector
+train = subset(data, split == TRUE)
+test = subset(data, split == FALSE)
+
+
+feats <- names(scaled.data)
+
+# Concatenate strings
+f <- paste(feats,collapse=' + ')
+f <- paste('Private ~',f)
+
+# Convert to formula
+f <- as.formula(f)
+
+f
+
+
+#install.packages('neuralnet')
+library(neuralnet)
+nn <- neuralnet(f,train,hidden=c(10,10,10),linear.output=FALSE)
+
+
+# Compute Predictions off Test Set
+predicted.nn.values <- compute(nn,test[2:18])
+
+# Check out net.result
+print(head(predicted.nn.values$net.result))
+
+
+predicted.nn.values$net.result <- sapply(predicted.nn.values$net.result,round,digits=0)
+
+table(test$Private,predicted.nn.values$net.result)