SimpleNN.Rmd

---
title: "SimpleNN"
output: html_document
---



```{r cars}
## Creating index variable 

# Read the Data
data = read.csv("data/cereals.csv", header=T)

# Random sampling
samplesize = 0.60 * nrow(data)
set.seed(80)
index = sample( seq_len ( nrow ( data ) ), size = samplesize )

```

```{r}
## Scale data for neural network

max = apply(data , 2 , max)
min = apply(data, 2 , min)
scaled = as.data.frame(scale(data, center = min, scale = max - min))
scaled
```

```{r}
# load library
library(neuralnet)

# creating training and test set
trainNN = scaled[index , ]
testNN = scaled[-index , ]

head(trainNN)

# fit neural network
set.seed(2)
NN = neuralnet(rating ~ calories + fat, trainNN, hidden = c(5,5,5) , linear.output = T )

str(NN)

# plot neural network
plot(NN, rep="best")
plotnet(NN)
```

```{r}
NN$weights
newdata = t(c(0.7, 0.4)) # truth rating .36
compute(NN, newdata)
```