input: 
  def predict_customer_lifetime_value(transactions, customer_input):
      """
      Given a dataset of customer past transactions and a new customer's data, 
      this function predicts the customer's lifetime value using a machine learning model.
      
      >>> example_transactions = pd.read_csv('example_transactions.csv')
      >>> predict_customer_lifetime_value(example_transactions, np.array([[10,100,5,...]]))
      Predicted customer lifetime value 2500.0
      """
      import pandas as pd
      import numpy as np
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
  
      X = transactions.iloc[:,:-1].values
      y = transactions.iloc[:,-1].values
      scaler = StandardScaler()
      X = scaler.fit_transform(X)
  
      model = RandomForestRegressor()
      model.fit(X, y)
  
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
      print('Predicted customer lifetime value', predicted_LTV)
  
output:     return predicted_LTV