input: 
  def predict_customer_ltv(file_path: str, customer_input: list) -> float:
      """ Predicts the customer's lifetime value based on past transactions using a 
      machine learning model.
      >>> predict_customer_ltv('transactions.csv', [[10,100,5,...]])
      1250.75
      """
      import pandas as pd
      import numpy as np
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
  
      # Preprocessing
      df = pd.read_csv(file_path)
      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([customer_input])
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
      return predicted_LTV
  
output:     return predicted_LTV