input: def predict_customer_LTV():
      """
      This function reads a CSV file containing customer transaction data, preprocesses the data, 
      trains a RandomForestRegressor model, and predicts the customer lifetime value (LTV) for 
      a new customer based on their past transactions.
      
      Example:
      >>> predict_customer_LTV()
      'Predicted customer lifetime value 500.23'
      """
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 dataset
      data = pd.read_csv('customer_transactions.csv')
  
      # Preprocess the data
      data['transaction_date'] = pd.to_datetime(data['transaction_date'])
      data['days_since_first_transaction'] = (data['transaction_date'] - data['transaction_date'].min()).dt.days
      data['total_spent'] = data.groupby('customer_id')['amount'].transform('sum')
      data['transaction_count'] = data.groupby('customer_id')['amount'].transform('count')
  
      # Feature selection
      features = ['days_since_first_transaction', 'transaction_count', 'total_spent']
      X = data[features]
      y = data['total_spent']
  
      # Train-test split
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train the model
      model = RandomForestRegressor(n_estimators=100, random_state=42)
      model.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = model.predict(X_test)
  
      # Evaluate the model
      mse = mean_squared_error(y_test, y_pred)
      print(f'Mean Squared Error: {mse}')
  
      # Predict LTV for a new customer
      new_customer_data = pd.DataFrame({
          'days_since_first_transaction': [30],
          'transaction_count': [5],
          'total_spent': [250]
      })
  
      predicted_ltv = model.predict(new_customer_data)
      return f'Predicted customer lifetime value {predicted_ltv[0]:.2f}'