input: Write a function to create a machine learning model in Python that predicts customer lifetime value (LTV) given a customer's past transactions. The function should use the provided data from a CSV file, preprocess it using standardization, train a RandomForestRegressor model, and then make predictions on new customer data.
  assert predict_cltv('transactions.csv', [[10, 100, 5, ...]]) == predicted_LTV
  assert predict_cltv('another_transactions.csv', [[5, 50, 2, ...]]) == another_predicted_LTV
  assert predict_cltv('empty_transactions.csv', [[10, 100, 5, ...]]) == "Error: No data in the input file"
  assert predict_cltv('faulty_format.csv', [[10, 100, 5, ...]]) == "Error: File format issue"
  assert predict_cltv('negative_transactions.csv', [[-10, -100, -5, ...]]) == "Error: Invalid input data"
  
  def predict_cltv(transactions_file, customer_input):
      # Assuming the function implementation is as described in the provided code snippet.
      pass
output: def predict_cltv(transactions_file, customer_input):
      try:
          import pandas as pd
          from sklearn.ensemble import RandomForestRegressor
          from sklearn.preprocessing import StandardScaler
          from sklearn.model_selection import train_test_split
  
          # Load the data
          df = pd.read_csv(transactions_file)
          if df.empty:
              return "Error: No data in the input file"
          
          # Preprocess the data
          X = df.drop('LTV', axis=1)
          y = df['LTV']
          scaler = StandardScaler()
          X_scaled = scaler.fit_transform(X)
          
          # Train the model
          X_train, _, y_train, _ = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
          model = RandomForestRegressor(random_state=42)
          model.fit(X_train, y_train)
          
          # Preprocess the input data
          customer_input_scaled = scaler.transform([customer_input[0]])
          
          # Make predictions
          prediction = model.predict(customer_input_scaled)
          return prediction[0]
      
      except pd.errors.EmptyDataError:
          return "Error: No data in the input file"
      except pd.errors.ParserError:
          return "Error: File format issue"
      except ValueError:
          return "Error: Invalid input data"