input: 
  def predict_customer_lifetime_value():
      """
      Build a machine learning model in Python that predicts the customer lifetime value (LTV) 
      given a customer's past transactions. The model uses a RandomForestRegressor for prediction.
      The function reads in a dataset of past transactions, preprocesses it, trains the model, 
      and then predicts the LTV for a new customer based on their data.
      
      >>> df = pd.read_csv('transactions.csv')
      >>> X = df.iloc[:,:-1].values
      >>> y = df.iloc[:,-1].values
      >>> scaler = StandardScaler()
      >>> X = scaler.fit_transform(X)
      >>> model = RandomForestRegressor()
      >>> model.fit(X, y)
      >>> customer_input = np.array([[10,100,5]]) # Example inputs
      >>> trans_scaled = scaler.transform(customer_input)
      >>> predicted_LTV = model.predict(trans_scaled)[0]
      >>> print(predicted_LTV > 0)
      True
      """
      import pandas as pd
      import numpy as np
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
  
      # Preprocessing
      df = pd.read_csv('transactions.csv')
      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
      customer_input = np.array([[10,100,5]]) # Example inputs
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
      print('Predicted customer lifetime value', predicted_LTV)
  
output:     return predicted_LTV