ANN.R

#Artificiel Neural Network

#import the dataset
dataset = read.csv('Churn_Modelling.csv')
dataset = dataset[4: 14]

# inconding categorical variable as factor
dataset$Geography = as.numeric(factor(dataset$Geography, 
                                levels = c('France','Spain','Germany'),
                                labels = c(1,2,3)))
dataset$Gender = as.numeric(factor(dataset$Gender, 
                        levels = c('Male','Female'),
                        labels = c(1,2)))

#training the dataset into the training set and the test set
library(caTools)
set.seed(123)
split = sample.split(dataset$Exited, SplitRatio = 0.8)
training_set= subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)

#Feature Scaling
training_set[-11] = scale(training_set[-11])
test_set[-11] = scale(test_set[-11])

#Fitting the ANN to the training set
install.packages('h2o')
library(h2o)
h2o.init(nthreads = -1)
classifier = h2o.deeplearning(y = 'Exited',
                              training_frame = as.h2o(training_set),
                              activation = 'Rectifier',
                              hidden = c(6,6),
                              epochs = 100,
                              train_samples_per_iteration = -2)

#Predicting the test result

prob_pred = h2o.predict(classifier, newdata = as.h2o(test_set[-11]))
y_pred =(prob_pred > 0.5)


#Making the Confusion Matrix
cm = table(test_set[-11], y_pred)