input: Write a python function to load historical stock data, fit a linear regression model, and make predictions for future stock prices using scikit-learn.
  ```python
  def predict_stock_prices(file_path, X_test):
      import numpy as np
      import pandas as pd
      from sklearn.linear_model import LinearRegression
  
      # Load the historical data
      data = pd.read_csv(file_path)
  
      # Prepare the feature and target variables
      X = data.iloc[:, :-1]
      y = data['Close']
  
      # Fit the linear regression model
      model = LinearRegression()
      model.fit(X, y)
  
      # Make predictions
      predictions = model.predict(X_test)
  
      return predictions
  
  # Example usage:
  # assert statements as test cases
  assert len(predict_stock_prices('stock-data.csv', np.array([[100, 200, 300], [150, 250, 350]]))) == 2
output: def predict_stock_prices(file_path, X_test):
      import numpy as np
      import pandas as pd
      from sklearn.linear_model import LinearRegression
  
      # Load the historical data
      data = pd.read_csv(file_path)
  
      # Prepare the feature and target variables
      X = data.iloc[:, :-1]
      y = data['Close']
  
      # Fit the linear regression model
      model = LinearRegression()
      model.fit(X, y)
  
      # Make predictions
      predictions = model.predict(X_test)
  
      return predictions
  
  # Example usage:
  # assert statements as test cases
  assert len(predict_stock_prices('stock-data.csv', np.array([[100, 200, 300], [150, 250, 350]]))) == 2