notebook.community

Edit and run 





In [1]:



    


## https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/



    











In [4]:



    


from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
numpy.random.seed(7)



    











In [7]:



    


dataset = numpy.loadtxt("pima-indians-diabetes.csv",delimiter =',')
dataset.shape



    


















    
Out[7]:








(768, 9)

















In [8]:



    


X = dataset[:,0:8]
Y = dataset[:,8]



    











In [9]:



    


def build_model():
    model = Sequential()
    model.add(Dense(12,input_dim=8,init = 'uniform',activation='relu'))
    '''
    Note this line:
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
1
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
It does a few things.

It defines the input layer as having 8 inputs.
It defines a hidden layer with 12 neurons, connected to the input layer that use relu activation function.
It initializes all weights using a sample of uniform random numbers.
    '''
    model.add(Dense(8,activation='relu'))
    model.add(Dense(1,activation='sigmoid'))
    model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
    return model



    











In [12]:



    


model = build_model()
## Fit the model
model.fit(X,Y,epochs=150,batch_size=10)

#Evaluate the model
scores = model.evaluate(X,Y)
print("\nEvaluation::::\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))



    


















    






Epoch 1/150
768/768 [==============================] - 0s - loss: 1.8208 - acc: 0.4440     
Epoch 2/150
768/768 [==============================] - 0s - loss: 0.8045 - acc: 0.6029     
Epoch 3/150
768/768 [==============================] - 0s - loss: 0.7044 - acc: 0.6393     
Epoch 4/150
768/768 [==============================] - 0s - loss: 0.6827 - acc: 0.6380     
Epoch 5/150
768/768 [==============================] - 0s - loss: 0.6652 - acc: 0.6536     
Epoch 6/150
768/768 [==============================] - 0s - loss: 0.6427 - acc: 0.6654     
Epoch 7/150
768/768 [==============================] - 0s - loss: 0.6452 - acc: 0.6563     
Epoch 8/150
768/768 [==============================] - 0s - loss: 0.6294 - acc: 0.6510     
Epoch 9/150
768/768 [==============================] - 0s - loss: 0.6250 - acc: 0.6576     
Epoch 10/150
768/768 [==============================] - ETA: 0s - loss: 0.5927 - acc: 0.668 - 0s - loss: 0.6158 - acc: 0.6706     
Epoch 11/150
768/768 [==============================] - 0s - loss: 0.6144 - acc: 0.6693     
Epoch 12/150
768/768 [==============================] - 0s - loss: 0.6094 - acc: 0.6589     
Epoch 13/150
768/768 [==============================] - 0s - loss: 0.6089 - acc: 0.6732     
Epoch 14/150
768/768 [==============================] - 0s - loss: 0.6060 - acc: 0.6576     
Epoch 15/150
768/768 [==============================] - 0s - loss: 0.6026 - acc: 0.6732     
Epoch 16/150
768/768 [==============================] - 0s - loss: 0.6047 - acc: 0.6628     
Epoch 17/150
768/768 [==============================] - 0s - loss: 0.5992 - acc: 0.6745     
Epoch 18/150
768/768 [==============================] - 0s - loss: 0.6022 - acc: 0.6706     
Epoch 19/150
768/768 [==============================] - 0s - loss: 0.5979 - acc: 0.6628     
Epoch 20/150
768/768 [==============================] - 0s - loss: 0.5977 - acc: 0.6680     
Epoch 21/150
768/768 [==============================] - 0s - loss: 0.5974 - acc: 0.6667     
Epoch 22/150
768/768 [==============================] - 0s - loss: 0.5942 - acc: 0.6771     
Epoch 23/150
768/768 [==============================] - 0s - loss: 0.5953 - acc: 0.6771     
Epoch 24/150
768/768 [==============================] - 0s - loss: 0.5955 - acc: 0.6823     
Epoch 25/150
768/768 [==============================] - 0s - loss: 0.5950 - acc: 0.6888     
Epoch 26/150
768/768 [==============================] - 0s - loss: 0.5928 - acc: 0.6732     
Epoch 27/150
768/768 [==============================] - 0s - loss: 0.5939 - acc: 0.6641     
Epoch 28/150
768/768 [==============================] - 0s - loss: 0.6055 - acc: 0.6693     
Epoch 29/150
768/768 [==============================] - 0s - loss: 0.5947 - acc: 0.6914     
Epoch 30/150
768/768 [==============================] - 0s - loss: 0.5914 - acc: 0.6745     
Epoch 31/150
768/768 [==============================] - 0s - loss: 0.5892 - acc: 0.6693     
Epoch 32/150
768/768 [==============================] - 0s - loss: 0.5883 - acc: 0.6758     
Epoch 33/150
768/768 [==============================] - 0s - loss: 0.5886 - acc: 0.6771     
Epoch 34/150
768/768 [==============================] - 0s - loss: 0.5953 - acc: 0.6758     
Epoch 35/150
768/768 [==============================] - 0s - loss: 0.5881 - acc: 0.6771     
Epoch 36/150
768/768 [==============================] - 0s - loss: 0.5869 - acc: 0.6862     
Epoch 37/150
768/768 [==============================] - 0s - loss: 0.5881 - acc: 0.6797     
Epoch 38/150
768/768 [==============================] - 0s - loss: 0.5873 - acc: 0.6810     
Epoch 39/150
768/768 [==============================] - 0s - loss: 0.5853 - acc: 0.6823     
Epoch 40/150
768/768 [==============================] - 0s - loss: 0.5900 - acc: 0.6797     
Epoch 41/150
768/768 [==============================] - 0s - loss: 0.5868 - acc: 0.6771     
Epoch 42/150
768/768 [==============================] - 0s - loss: 0.5833 - acc: 0.6784     
Epoch 43/150
768/768 [==============================] - 0s - loss: 0.5840 - acc: 0.6862     
Epoch 44/150
768/768 [==============================] - 0s - loss: 0.5858 - acc: 0.6849     
Epoch 45/150
768/768 [==============================] - 0s - loss: 0.5873 - acc: 0.6823     
Epoch 46/150
768/768 [==============================] - 0s - loss: 0.5875 - acc: 0.6862     
Epoch 47/150
768/768 [==============================] - 0s - loss: 0.5859 - acc: 0.6823     
Epoch 48/150
768/768 [==============================] - 0s - loss: 0.5868 - acc: 0.6901     
Epoch 49/150
768/768 [==============================] - 0s - loss: 0.5848 - acc: 0.6797     
Epoch 50/150
768/768 [==============================] - 0s - loss: 0.5800 - acc: 0.6888     
Epoch 51/150
768/768 [==============================] - 0s - loss: 0.5860 - acc: 0.6849     
Epoch 52/150
768/768 [==============================] - 0s - loss: 0.5783 - acc: 0.6940     
Epoch 53/150
768/768 [==============================] - 0s - loss: 0.5836 - acc: 0.6901     
Epoch 54/150
768/768 [==============================] - 0s - loss: 0.5807 - acc: 0.6836     
Epoch 55/150
768/768 [==============================] - 0s - loss: 0.5859 - acc: 0.6810     
Epoch 56/150
768/768 [==============================] - 0s - loss: 0.5818 - acc: 0.6914     
Epoch 57/150
768/768 [==============================] - 0s - loss: 0.5809 - acc: 0.6797     
Epoch 58/150
768/768 [==============================] - 0s - loss: 0.5793 - acc: 0.6875     
Epoch 59/150
768/768 [==============================] - 0s - loss: 0.5807 - acc: 0.6862     
Epoch 60/150
768/768 [==============================] - 0s - loss: 0.5784 - acc: 0.6797     
Epoch 61/150
768/768 [==============================] - 0s - loss: 0.5787 - acc: 0.6862     
Epoch 62/150
768/768 [==============================] - 0s - loss: 0.5793 - acc: 0.6914     
Epoch 63/150
768/768 [==============================] - 0s - loss: 0.5780 - acc: 0.6862     
Epoch 64/150
768/768 [==============================] - 0s - loss: 0.5840 - acc: 0.6901     
Epoch 65/150
768/768 [==============================] - 0s - loss: 0.5797 - acc: 0.6836     
Epoch 66/150
768/768 [==============================] - 0s - loss: 0.5765 - acc: 0.6927     
Epoch 67/150
768/768 [==============================] - 0s - loss: 0.5839 - acc: 0.6875     
Epoch 68/150
768/768 [==============================] - 0s - loss: 0.5855 - acc: 0.6875     
Epoch 69/150
768/768 [==============================] - 0s - loss: 0.5839 - acc: 0.6797     
Epoch 70/150
768/768 [==============================] - 0s - loss: 0.5777 - acc: 0.6927     
Epoch 71/150
768/768 [==============================] - 0s - loss: 0.5763 - acc: 0.6823     
Epoch 72/150
768/768 [==============================] - 0s - loss: 0.5752 - acc: 0.6888     
Epoch 73/150
768/768 [==============================] - 0s - loss: 0.5760 - acc: 0.6901     
Epoch 74/150
768/768 [==============================] - 0s - loss: 0.5745 - acc: 0.6940     
Epoch 75/150
768/768 [==============================] - 0s - loss: 0.5808 - acc: 0.6888     
Epoch 76/150
768/768 [==============================] - 0s - loss: 0.5848 - acc: 0.6953     
Epoch 77/150
768/768 [==============================] - 0s - loss: 0.5793 - acc: 0.6784     
Epoch 78/150
768/768 [==============================] - 0s - loss: 0.5771 - acc: 0.6992     
Epoch 79/150
768/768 [==============================] - 0s - loss: 0.5739 - acc: 0.6836     
Epoch 80/150
768/768 [==============================] - 0s - loss: 0.5737 - acc: 0.6927     
Epoch 81/150
768/768 [==============================] - 0s - loss: 0.5771 - acc: 0.6927     
Epoch 82/150
768/768 [==============================] - 0s - loss: 0.5739 - acc: 0.6862     
Epoch 83/150
768/768 [==============================] - 0s - loss: 0.5781 - acc: 0.6823     
Epoch 84/150
768/768 [==============================] - 0s - loss: 0.5771 - acc: 0.6914     
Epoch 85/150
768/768 [==============================] - 0s - loss: 0.5759 - acc: 0.6914     
Epoch 86/150
768/768 [==============================] - 0s - loss: 0.5769 - acc: 0.6771     
Epoch 87/150
768/768 [==============================] - 0s - loss: 0.5750 - acc: 0.6888     
Epoch 88/150
768/768 [==============================] - 0s - loss: 0.5713 - acc: 0.6914     
Epoch 89/150
768/768 [==============================] - 0s - loss: 0.5743 - acc: 0.6940     
Epoch 90/150
768/768 [==============================] - 0s - loss: 0.5736 - acc: 0.6875     
Epoch 91/150
768/768 [==============================] - 0s - loss: 0.5730 - acc: 0.6888     
Epoch 92/150
768/768 [==============================] - 0s - loss: 0.5719 - acc: 0.6914     
Epoch 93/150
768/768 [==============================] - 0s - loss: 0.5894 - acc: 0.6836     
Epoch 94/150
768/768 [==============================] - 0s - loss: 0.5721 - acc: 0.6875     
Epoch 95/150
768/768 [==============================] - 0s - loss: 0.5708 - acc: 0.6901     
Epoch 96/150
768/768 [==============================] - 0s - loss: 0.5781 - acc: 0.6940     
Epoch 97/150
768/768 [==============================] - 0s - loss: 0.5723 - acc: 0.6888     
Epoch 98/150
768/768 [==============================] - 0s - loss: 0.5684 - acc: 0.6901     
Epoch 99/150
768/768 [==============================] - 0s - loss: 0.5693 - acc: 0.6914     
Epoch 100/150
768/768 [==============================] - 0s - loss: 0.5721 - acc: 0.6966     
Epoch 101/150
768/768 [==============================] - 0s - loss: 0.5777 - acc: 0.6849     
Epoch 102/150
768/768 [==============================] - 0s - loss: 0.5682 - acc: 0.6992     
Epoch 103/150
768/768 [==============================] - 0s - loss: 0.5752 - acc: 0.6875     
Epoch 104/150
768/768 [==============================] - 0s - loss: 0.5665 - acc: 0.6953     
Epoch 105/150
768/768 [==============================] - 0s - loss: 0.5689 - acc: 0.6849     
Epoch 106/150
768/768 [==============================] - 0s - loss: 0.5726 - acc: 0.6901     
Epoch 107/150
768/768 [==============================] - 0s - loss: 0.5750 - acc: 0.6849     
Epoch 108/150
768/768 [==============================] - 0s - loss: 0.5729 - acc: 0.6901     
Epoch 109/150
768/768 [==============================] - 0s - loss: 0.5675 - acc: 0.6914     
Epoch 110/150
768/768 [==============================] - 0s - loss: 0.5665 - acc: 0.6901     
Epoch 111/150
768/768 [==============================] - 0s - loss: 0.5715 - acc: 0.6953     
Epoch 112/150
768/768 [==============================] - 0s - loss: 0.5672 - acc: 0.6927     
Epoch 113/150
768/768 [==============================] - 0s - loss: 0.5687 - acc: 0.6901     
Epoch 114/150
768/768 [==============================] - 0s - loss: 0.5707 - acc: 0.6992     
Epoch 115/150
768/768 [==============================] - 0s - loss: 0.5628 - acc: 0.6888     
Epoch 116/150
768/768 [==============================] - 0s - loss: 0.5675 - acc: 0.6953     
Epoch 117/150
768/768 [==============================] - 0s - loss: 0.5708 - acc: 0.6992     
Epoch 118/150
768/768 [==============================] - 0s - loss: 0.5640 - acc: 0.6979     
Epoch 119/150
768/768 [==============================] - 0s - loss: 0.5651 - acc: 0.6992     
Epoch 120/150
768/768 [==============================] - 0s - loss: 0.5694 - acc: 0.6875     
Epoch 121/150
768/768 [==============================] - 0s - loss: 0.5610 - acc: 0.6992     
Epoch 122/150
768/768 [==============================] - 0s - loss: 0.5705 - acc: 0.6901     
Epoch 123/150
768/768 [==============================] - 0s - loss: 0.5627 - acc: 0.7018     
Epoch 124/150
768/768 [==============================] - 0s - loss: 0.5610 - acc: 0.6966     
Epoch 125/150
768/768 [==============================] - 0s - loss: 0.5686 - acc: 0.6927     
Epoch 126/150
768/768 [==============================] - 0s - loss: 0.5621 - acc: 0.6927     
Epoch 127/150
768/768 [==============================] - 0s - loss: 0.5653 - acc: 0.6875     
Epoch 128/150
768/768 [==============================] - 0s - loss: 0.5608 - acc: 0.6992     
Epoch 129/150
768/768 [==============================] - 0s - loss: 0.5614 - acc: 0.6901     
Epoch 130/150
768/768 [==============================] - 0s - loss: 0.5683 - acc: 0.6875     
Epoch 131/150
768/768 [==============================] - 0s - loss: 0.5713 - acc: 0.6901     
Epoch 132/150
768/768 [==============================] - 0s - loss: 0.5614 - acc: 0.7005     
Epoch 133/150
768/768 [==============================] - 0s - loss: 0.5602 - acc: 0.7057     
Epoch 134/150
768/768 [==============================] - 0s - loss: 0.5631 - acc: 0.6888     
Epoch 135/150
768/768 [==============================] - 0s - loss: 0.5627 - acc: 0.7031     
Epoch 136/150
768/768 [==============================] - 0s - loss: 0.5637 - acc: 0.6927     
Epoch 137/150
768/768 [==============================] - 0s - loss: 0.5612 - acc: 0.6914     
Epoch 138/150
768/768 [==============================] - 0s - loss: 0.5621 - acc: 0.6901     
Epoch 139/150
768/768 [==============================] - 0s - loss: 0.5611 - acc: 0.7044     
Epoch 140/150
768/768 [==============================] - 0s - loss: 0.5610 - acc: 0.7018     
Epoch 141/150
768/768 [==============================] - 0s - loss: 0.5619 - acc: 0.6979     
Epoch 142/150
768/768 [==============================] - 0s - loss: 0.5560 - acc: 0.7070     
Epoch 143/150
768/768 [==============================] - 0s - loss: 0.5603 - acc: 0.6953     
Epoch 144/150
768/768 [==============================] - 0s - loss: 0.5577 - acc: 0.6979     
Epoch 145/150
768/768 [==============================] - 0s - loss: 0.5682 - acc: 0.6927     
Epoch 146/150
768/768 [==============================] - 0s - loss: 0.5600 - acc: 0.7005     
Epoch 147/150
768/768 [==============================] - 0s - loss: 0.5572 - acc: 0.6992     
Epoch 148/150
768/768 [==============================] - 0s - loss: 0.5600 - acc: 0.6862     
Epoch 149/150
768/768 [==============================] - 0s - loss: 0.5590 - acc: 0.7005     
Epoch 150/150
768/768 [==============================] - 0s - loss: 0.5580 - acc: 0.6940     
 32/768 [>.............................] - ETA: 0s
Evaluation::::
acc: 70.57%

















In [14]:



    


predictions = model.predict(X)
## round predictions
rounded = [round(x[0]) for x in predictions]
print(rounded)



    


















    






[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]

















In [ ]:

Content source: prashantas/MyDataScience

Similar notebooks:

notebook.community | gallery | about