#simple project for training model to #classify the patient if he had diabetes #or not
import numpy as np from pandas import read_csv url = "file:///G:/nural%20network/datasets/archive/diabetes.csv" data= read_csv(url)
input_data = data.values[:,:-1] output_data= data.values[:,-1] test_data= data.values[600:,:-1]
from keras.models import Sequential from keras.layers import Dense
model= Sequential() model.add(Dense(12,input_dim=8,activation='relu')) model.add(Dense(10,activation='relu')) model.add(Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
model.fit(input_data,output_data,epochs=620,batch_size=70)
accuracy=model.evaluate(input_data,output_data)
predections= (model.predict(input_data)>0.5).astype(float)
print(predections)
for i in range(20): print(predections[i],output_data[i])