text: import keras, pandas
  import tensorflow
  import numpy
  from keras.regularizers import l2
  from keras.layers import Dense
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.model_selection import train_test_split
  file = "simplified_only_benign_and_pathogenic.csv"
  original_data = pandas.read_csv(file)
  print(original_data.head())
  data = original_data[:800]
  data = data.drop(columns=["Unnamed: 0"])
  print(data.head())
  X = data.drop(columns=["simplified.significance"])
  Y = data["simplified.significance"]
  
  scaler = MinMaxScaler(feature_range=(0, 1))
  rescaledX = scaler.fit_transform(X)
  
  x_train, x_test, y_train, y_test = train_test_split(rescaledX, Y)
  
  model = keras.models.Sequential()
  model.add(Dense(100, activation = "relu", input_dim=3, kernel_regularizer=l2(0.01)))
  model.add(Dense(1, activation="sigmoid"))
  model.summary()
  model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
  
  
  
  check_point = keras.callbacks.ModelCheckpoint("checkpoint2.h5", monitor='val_loss', verbose=1, save_best_only=True,
                                                       save_weights_only=False, mode='auto', period=1)
  early_callback = keras.callbacks.EarlyStopping(monitor='loss', mode='auto', min_delta=0.001, patience=5,
                                                        verbose=1)
  callbacks_list = [check_point, early_callback]
  
  info = model.fit(x_train, y_train, batch_size=32, epochs=100, verbose=1, callbacks=callbacks_list, validation_data=(x_test, y_test), shuffle=True)
  print("Training Accuracy ", numpy.mean(info.history['accuracy']))
  print("Validation Accuracy ", numpy.mean(info.history['val_accuracy']))
  
  test_data = original_data[800:]
  test_data = test_data.drop(columns=["Unnamed: 0"])
  X_eval = test_data.drop(columns=["simplified.significance"])
  Y_eval = test_data["simplified.significance"]
  result = model.evaluate(X_eval, Y_eval, batch_size=128)
  print('test loss, test acc:', result)
  