input: def predict_customer_lifetime_value():
      """ Create a machine learning model in Python that predecits customer lifetime value given a customer's past transactions.
      Example:
      for predict_customer_lifetime_value(), if 'transactions.csv' contains [['2020-01-01', 500], ['2020-02-01', 550], ['2020-03-01', 120]], and customer_input is [['10', '100', '5', ...]],
      the output will print 'Predicted customer lifetime value' followed by the predicted value for the given customer input data.
      """
output:     
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestRegressor
      from sklearn.metrics import mean_squared_error
  
      # Load the transaction data
      transactions = pd.read_csv('transactions.csv', header=None, names=['Date', 'Amount'])
  
      # Prepare the data
      transactions['Date'] = pd.to_datetime(transactions['Date'])
      transactions['Amount'] = transactions['Amount'].astype(float)
  
      # Calculate total spending
      total_spending = transactions['Amount'].sum()
  
      # Create a simple model to predict lifetime value
      X = transactions[['Amount']]
      y = transactions['Amount']
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = RandomForestRegressor(n_estimators=100, random_state=42)
      model.fit(X_train, y_train)
  
      # Predict the lifetime value for the given customer input
      customer_input = pd.DataFrame(customer_input, columns=['Amount'])
      predicted_value = model.predict(customer_input)
  
      # Print the predicted customer lifetime value
      print('Predicted customer lifetime value:', predicted_value[0])
  