input: def predict_customer_lifetime_value(transactions_csv_path: str, customer_input: np.ndarray) -> float:
      """Given the path to a CSV file containing customer transaction data, this function
      preprocesses the data, trains a machine learning model using RandomForestRegressor,
      and then predicts the customer lifetime value (LTV) for a given customer input.
      
      >>> predict_customer_lifetime_value('transactions.csv', np.array([[10, 100, 5, ...]]))
      532.12  # Example output: a predicted LTV
      """
      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_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(transactions_csv_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