input: Below is an instruction that describes a task. Write a response that appropriately completes the request.
  
  ### Instruction:
  Create a machine learning model in Python that predicts customer lifetime value given a customer's past transactions.
  
  ### Input:
  Not applicable
  
  ### Output:
  def predict_customer_ltv():
      """ Create a machine learning model in Python that predicts customer lifetime value 
      given a customer's past transactions. The model uses RandomForestRegressor from 
      sklearn and requires preprocessing of data using StandardScaler. 
      
      Example:
      >>> predict_customer_ltv()
      Predicted customer lifetime value 1234.5
      """
      import pandas as pd
      import numpy as np
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
  
      # Preprocessing
      df = pd.read_csv('transactions.csv')
      X = df.iloc[:,:-1].values
      y = df.iloc[:,-1].values
      scaler = StandardScaler()
      X = scaler.fit_transform(X)
  
      # Model Training
      model = RandomForestRegressor()
      model.fit(X, y)
  
      # Prediction
      customer_input = np.array([[10,100,5,...]]) # provide data related to past transactions
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
      print('Predicted customer lifetime value', predicted_LTV)
      
      return predicted_LTV
output:     import pandas as pd
      import numpy as np
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
  
      # Preprocessing
      df = pd.read_csv('transactions.csv')
      X = df.iloc[:,:-1].values
      y = df.iloc[:,-1].values
      scaler = StandardScaler()
      X = scaler.fit_transform(X)
  
      # Model Training
      model = RandomForestRegressor()
      model.fit(X, y)
  
      # Prediction
      customer_input = np.array([[10,100,5]]) # provide data related to past transactions
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
      print('Predicted customer lifetime value', predicted_LTV)
      
      return predicted_LTV