input: Write a python function to load data into a DataFrame, fit a linear regression model, and make predictions for future stock prices.
  assert predict_stock_prices([[145, 285, 181], [149, 290, 184]])==[predicted_values]
  assert predict_stock_prices([[150, 300, 190], [155, 305, 195]])==[predicted_values]
  assert predict_stock_prices([[160, 310, 200], [165, 315, 205]])==[predicted_values]
output: import pandas as pd
  from sklearn.linear_model import LinearRegression
  
  def predict_stock_prices(data):
      # Convert the input data to a DataFrame
      df = pd.DataFrame(data, columns=['feature1', 'feature2', 'feature3'])
      
      # Suppose we have historical data for training
      # This is a placeholder for actual historical data
      historical_data = {
          'feature1': [140, 145, 150, 155, 160],
          'feature2': [280, 285, 290, 295, 300],
          'feature3': [180, 185, 190, 195, 200],
          'stock_price': [200, 210, 220, 230, 240]
      }
      
      historical_df = pd.DataFrame(historical_data)
      
      # Features and target variable
      X = historical_df[['feature1', 'feature2', 'feature3']]
      y = historical_df['stock_price']
      
      # Fit the linear regression model
      model = LinearRegression()
      model.fit(X, y)
      
      # Predict future stock prices
      predictions = model.predict(df)
      
      return predictions.tolist()