    import numpy as np
    import pandas as pd

#### Keras is the simplest ML library, so I hope you enjoy it

    from keras.models import Sequential
    from keras.layers import Dense

    model = Sequential()

    model.add(Dense(units=2, activation='relu', input_dim=2))
    model.add(Dense(units=1))

    model.compile(
        loss='mean_squared_error',
        optimizer='sgd',
        metrics=['accuracy']
    )

1. first, we created a `sequential model`
2. then, we added two `dense layer(actually neural network)` for it. the first layer has one neural node, the second has one neural node, too.
3. sgd = Stochastic gradient descent

#### After that, let us feed data into our model

    model.fit(x, y, epochs=100, batch_size=1)

#### Then do a prediction

    model.predict(np.array([[170, 96]]))

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