input: Write a python function to predict tomorrow's Bitcoin price using a neural network model.
  assert isinstance(bitcoinPricePrediction([[1,2,3],[4,5,6],[7,8,9]]), float)
  assert isinstance(bitcoinPricePrediction([[100,200,300],[400,500,600],[700,800,900]]), float)
  assert isinstance(bitcoinPricePrediction([[50,60,70],[80,90,100],[110,120,130]]), float)
output: def bitcoinPricePrediction(data):
      import numpy as np
      from sklearn.neural_network import MLPRegressor
      
      X = np.array(data[:-1])
      y = np.array(data[1:])
      
      X = X.reshape(-1, X.shape[-1])
      y = y.reshape(-1, y.shape[-1])
      
      model = MLPRegressor(hidden_layer_sizes=(100,), max_iter=5000, random_state=1)
      model.fit(X, y)
      
      tomorrow_input = np.array(data[-1]).reshape(1, -1)
      prediction = model.predict(tomorrow_input)
      
      return float(prediction[0, -1])