input: 
  def predict_customer_lifetime_value(transactions_file: str, customer_input: np.array) -> float:
      """ This function creates a machine learning model that predicts customer lifetime value (LTV)
      based on past transactions. It preprocesses the data, trains a model, 
      and then predicts the LTV for a given set of transaction data.
  
      >>> predict_customer_lifetime_value('transactions.csv', np.array([[10, 100, 5]]))
      Predicted customer lifetime value: [some_value]
      """
      import pandas as pd
      import numpy as np
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
      
      # Preprocessing
      df = pd.read_csv(transactions_file)
      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
      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_file)
      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
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
      print('Predicted customer lifetime value:', predicted_LTV)
      return predicted_LTV