input: Write a python function to prepare the data for training a RandomForestClassifier to predict stock changes.
  assert prepare_data([1, 2, 3, 4, 5]) == (pd.DataFrame({0: [1, 2, 3, 4, 5], 'Target': ['Increase', 'Increase', 'Increase', 'Increase', 'Decrease']}), pd.Series(['Increase', 'Increase', 'Increase', 'Increase', 'Decrease']))
  assert prepare_data([10, 15, 13, 20, 18]) == (pd.DataFrame({0: [10, 15, 13, 20, 18], 'Target': ['Increase', 'Decrease', 'Increase', 'Decrease', 'Decrease']}), pd.Series(['Increase', 'Decrease', 'Increase', 'Decrease', 'Decrease']))
  assert prepare_data([100, 90, 95, 85, 80]) == (pd.DataFrame({0: [100, 90, 95, 85, 80], 'Target': ['Decrease', 'Increase', 'Decrease', 'Decrease', 'Decrease']}), pd.Series(['Decrease', 'Increase', 'Decrease', 'Decrease', 'Decrease']))
output: import pandas as pd
  
  def prepare_data(prices):
      data = pd.DataFrame(prices)
      target = []
      for i in range(1, len(prices)):
          if prices[i] > prices[i-1]:
              target.append('Increase')
          else:
              target.append('Decrease')
      target.append('Decrease' if prices[-1] <= prices[-2] else 'Increase')
      data['Target'] = target
      return data, data['Target']