input: 
  from sklearn.preprocessing import StandardScaler
  from sklearn.ensemble import RandomForestRegressor
  
  def predict_customer_ltv(transaction_file: str, customer_input: np.ndarray) -> float:
      """
      Given a CSV file containing past customer transactions and a customer input array representing a new customer's past transactions,
      this function predicts the customer's lifetime value (LTV) using a machine learning model.
      
      >>> predict_customer_ltv('transactions.csv', np.array([[10,100,5,2,3,1]]))
      1200.5
      >>> predict_customer_ltv('transactions.csv', np.array([[5,50,3,1,0,0]]))
      750.0
      """
      import pandas as pd
      import numpy as np
  
      # Preprocessing
      df = pd.read_csv(transaction_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]
      return predicted_LTV
  
output:     import pandas as pd
      import numpy as np
  
      # Preprocessing
      df = pd.read_csv(transaction_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]
      return predicted_LTV