def accuracies():
data = dataProcessing()
accuracies = []
X_train, X_test, y_train, y_test = train_test_split(data[0],
data[1],
test_size=0.2,
random_state=1234)
# all model accuracies except neural network
for i in range(1, 5):
model = models.classifierModel(X_train, y_train, i)
accuracies.append(
round(models.modelAccuracies(y_test, model, X_test), 2))
# neural network accuracy
model = models.classifierModel(X_train, y_train, 5)
accuracies.append(round(model[0].history['accuracy'][0] * 100, 2))
return {'accuracies': accuracies}
def randomForest():
data = dataProcessing()
X_train, X_test, y_train, y_test = train_test_split(data[0],
data[1],
test_size=0.2,
random_state=1234)
model = models.classifierModel(X_train, y_train, 3)
res = request.get_json()
results = dataPrediction(res, model)
return {'result': str(results)}
def neuralNetwork():
data = dataProcessing()
X_train, X_test, y_train, y_test = train_test_split(data[0],
data[1],
test_size=0.2,
random_state=1234)
model = models.classifierModel(X_train, y_train, 5)
res = request.get_json()
input_pred = model[1].predict([res])
print(input_pred)
return {'result': str(input_pred)}