input: Write a function to create a machine learning model in Python that predicts customer lifetime value given a customer's past transactions. The function should include preprocessing steps and model training using RandomForestRegressor. Finally, it should predict the customer lifetime value for a given input. If the input is empty, the function should return 0.
  def predict_ltv(transactions_csv):
      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)
      if df.empty:
          return 0
      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]
      return predicted_LTV
  
  assert predict_ltv('non_empty_transactions.csv') != 0
  assert predict_ltv('empty_transactions.csv') == 0
output: def predict_ltv(transactions_csv):
      import pandas as pd
      import numpy as np
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
      
      df = pd.read_csv(transactions_csv)
      if df.empty:
          return 0
      X = df.iloc[:,:-1].values
      y = df.iloc[:,-1].values
      scaler = StandardScaler()
      X = scaler.fit_transform(X)
  
      model = RandomForestRegressor()
      model.fit(X, y)
  
      customer_input = np.array([[10,100,5]])
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
      return predicted_LTV