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 preprocess the data, train a RandomForestRegressor model, and return the predicted lifetime value for a given customer.
  assert predict_customer_ltv("transactions.csv", [[10, 100, 5]]) == predicted_ltv
  assert predict_customer_ltv("transactions.csv", [[5, 50, 2]]) == predicted_ltv_2
  assert predict_customer_ltv("transactions.csv", [[1, 20, 1]]) == predicted_ltv_3
  assert predict_customer_ltv("transactions.csv", [[0, 0, 0]]) == predicted_ltv_4 
  assert predict_customer_ltv("transactions.csv", [[20, 200, 10]]) == predicted_ltv_5
output: def predict_customer_ltv(file_path, new_transactions):
      import pandas as pd
      from sklearn.ensemble import RandomForestRegressor
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
  
      # Load the data
      df = pd.read_csv(file_path)
      
      # Preprocess the data
      X = df.drop('lifetime_value', axis=1)
      y = df['lifetime_value']
      
      # Split the data
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Scale the data
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      
      # Train the model
      model = RandomForestRegressor(random_state=42)
      model.fit(X_train, y_train)
      
      # Preprocess new transactions
      new_transactions = scaler.transform(new_transactions)
      
      # Predict lifetime value
      predicted_ltv = model.predict(new_transactions)
      
      return predicted_ltv[0]