code: import pandas
  from keras.models import Sequential
  from keras.layers.core import Dense, Activation
  from keras.callbacks import TensorBoard
  # load dataset
  from sklearn.model_selection import train_test_split
  import pandas as pd
  
  dataset = pd.read_csv("framingham.csv", header=None).values
  import numpy as np
  
  X_train, X_test, Y_train, Y_test = train_test_split(dataset[:,0:15], dataset[:,15],
                                                      test_size=0.33, random_state=87)
  
  np.random.seed(100)
  nnokay = Sequential() # create model
  nnokay.add(Dense(20, input_dim=15, activation='tanh')) # hidden layer
  nnokay.add(Dense(30, activation='tanh')) #add whole layer
  nnokay.add(Dense(60, activation='tanh'))
  nnokay.add(Dense(20, activation='tanh'))
  nnokay.add(Dense(15, activation='tanh'))
  nnokay.add(Dense(60, activation='tanh'))
  nnokay.add(Dense(1, activation='tanh')) # output layer
  nnokay.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])
  
  nnokay.fit(X_train, Y_train, epochs=250, verbose=0,
                                      callbacks=[TensorBoard(log_dir = '/tmp/auto')])
  #print(nnokay.summary())
  #print(nnokay.evaluate(X_test, Y_test, verbose=0))
  
  score = nnokay.evaluate(X_test, Y_test)
  print('test accuracy', score[1])
  
  
apis: pandas.read_csv numpy.random.seed sklearn.model_selection.train_test_split