input: 
  def predict_customer_lifetime_value(data_path: str, customer_input: np.ndarray) -> float:
      """ Predict customer lifetime value (LTV) based on past transaction data and a given customer's past transactions.
      The function preprocesses the data using StandardScaler and trains a RandomForestRegressor model. It then predicts the LTV for the given customer input.
      >>> predict_customer_lifetime_value('transactions.csv', np.array([[10, 100, 5]]))
      500.0
      >>> predict_customer_lifetime_value('transactions_alt.csv', np.array([[20, 200, 10]]))
      1000.0
      """
      import pandas as pd
      import numpy as np
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
      
      # Preprocessing
      df = pd.read_csv(data_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
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
      return predicted_LTV
  
output:     import pandas as pd
      import numpy as np
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
      
      # Preprocessing
      df = pd.read_csv(data_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
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
      return predicted_LTV