input: 
  def predict_customer_ltv(input_data: np.ndarray) -> float:
      """
      Predicts the customer lifetime value (LTV) based on past transaction data using a machine learning model.
      The model is a trained Random Forest Regressor and the input data should be preprocessed using the same
      StandardScaler that was used during training.
      The function takes an input array `input_data` which represents the past transactions of a customer and
      returns the predicted LTV.
      
      >>> predict_customer_ltv(np.array([[10, 100, 5, ...]]))
      750.5
      """
      # Preprocessing
      scaler = StandardScaler()
      model = RandomForestRegressor()
      df = pd.read_csv('transactions.csv')
      X_train = df.iloc[:,:-1].values
      y_train = df.iloc[:,-1].values
      scaler.fit(X_train)
      X_train_scaled = scaler.transform(X_train)
      model.fit(X_train_scaled, y_train)
      
      # Prediction
      input_scaled = scaler.transform(input_data)
      predicted_LTV = model.predict(input_scaled)[0]
      return predicted_LTV
  
output:     import numpy as np
      import pandas as pd
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor