Load the necessary libraries

  Load the data set

  Convert the categorical variables to equivalent numeric classes

  Split the data set as train set and test set

  Initialize the keras sequential model

  Build the model with input layers,hidden layers and output layer as per the data size along with the activation function(here 1 I/P layer with relu activation and 1 O/P layer with no activation(for linear))

  Compile the model with required loss,metrics and optimizer(here loss=mean square error,optimizer=adam,metrics=accuracy)

  Fit the model using the train set

  Predict using the test set

  Evaluate the metrics

library(caret)
library(keras)
data=read.csv(‘/…/weight-height.csv’)
#Setting seed to reproduce results of random sampling data
set.seed(100)
#Train set
train_ind<-createDataPartition(data$Weight,p=0.8,list=FALSE) train_data<-data[train_ind,] xtrain=train_data[,2] ytrain=train_data$Weight #Test set test=data[-train_ind,] xtest=test$Height ytest=test$Weight #defining a keras sequential model model <- keras_model_sequential() #Define a neural network with 1 input layer with 100 neurons and 1 output layer model %>%
layer_dense(units = 100, input_shape = 1) %>%
layer_dropout(rate=0.4)%>%
layer_activation(activation = ‘relu’) %>%
layer_dense(units = 1)

#compiling the defined model with metric = accuracy and optimiser as adam.
model %>% compile(
loss = ‘mean_squared_error’,
optimizer = ‘adam’,
metrics = ‘accuracy’
)
#Summary of the model
summary(model)

#fitting the model on the training dataset
model %>% fit(xtrain, ytrain, epochs = 10, batch_size = 128)

#Predict using the Test data
yt=predict(model,xtest)

#Calculate the RMSE value
e=(ytest)-(yt)
err=e*e
rmse=sqrt(mean(err))
cat(“\nRoot mean square value is “,rmse)
y_test=data.frame(Weight=test$Weight)
#Calculate the R-Squared value
cat(“\nThe R-Sqaured Value : “,cor(yt,y_test) ^ 2)