input: Write a function in Python that creates a machine learning model to predict customer lifetime value (CLV) using a RandomForestRegressor based on the customer's past transactions. The function should preprocess the data by scaling it using StandardScaler and then train the model on the preprocessed data. Provide a sample input for a customer's past transactions and return the predicted CLV.
  
  
  import pandas as pd
  import numpy as np
  
  from sklearn.preprocessing import StandardScaler
  from sklearn.ensemble import RandomForestRegressor
  
  def predict_clv(csv_file, customer_input):
      # Preprocessing
      df = pd.read_csv(csv_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
  
  # Test cases
  assert predict_clv('transactions.csv', np.array([[10,100,5]])) > 0
  assert predict_clv('transactions.csv', np.array([[1,10,1]])) > 0
  assert predict_clv('transactions.csv', np.array([[50,200,10]])) > 0
  
output: def predict_clv(csv_file, customer_input):
      df = pd.read_csv(csv_file)
      X = df.iloc[:,:-1].values
      y = df.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]
      return predicted_LTV