Neural Network

Packages

Pip install scipy
Pip install Theano
Pip install keras
Pip install tensorflow


From numpy import loadtxt
From keras.models import Sequential
From keras.layers import Dense

Variable information

Input Variables (X):

1. Number of times pregnant
2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test
3. Diastolic blood pressure (mm Hg)
4. Triceps skinfold thickness (mm)
5. 2-Hour serum insulin (mu U/ml)
6. Body mass index (weight in kg/(height in m)^2)
7. Diabetes pedigree function
8. Age (years)

Output Variables (y):

1. Class variable (0 or 1)

Split Array into an input and output

# load the dataset
dataset = loadtxt('pima-indians-diabetes.csv', delimiter=',')
# split into input (X) and output (y) variables
X = dataset[:,0:8]
y = dataset[:,8]

Define and compile the Keras model

# define the keras model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# compile the keras model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

Fit model on data and evaluate

# fit the keras model on the dataset
model.fit(X, y, epochs=150, batch_size=10)

# evaluate the keras model
_, accuracy = model.evaluate(X, y)
print('Accuracy: %.2f' % (accuracy*100))

Predictions

# make probability predictions with the model
predictions = model.predict(X)
# round predictions 
rounded = [round(x[0]) for x in predictions]
# make class predictions with the model
predictions = model.predict_classes(X)

# summarize the first 5 cases
for i in range(5):
	print('%s => %d (expected %d)' % (X[i].tolist(), predictions[i], y[i]))

Conclusion

Expected that >76% of rows will be correctly predicted based on the performance of the model