input: Write a python function to predict stock prices using linear regression.
  assert abs(predict_stock_prices('stock_prices.csv')[0] - 123.45) < 1e-2
  assert abs(predict_stock_prices('stock_prices.csv')[1] - 678.90) < 1e-2
output: import pandas as pd
  from sklearn.linear_model import LinearRegression
  
  def predict_stock_prices(file_path):
      # Load the dataset
      data = pd.read_csv(file_path)
      
      # Assuming the dataset has columns 'Date' and 'Price'
      data['Date'] = pd.to_datetime(data['Date'])
      data['Date_ordinal'] = data['Date'].map(pd.Timestamp.toordinal)
      
      X = data[['Date_ordinal']]
      y = data['Price']
      
      # Train the linear regression model
      model = LinearRegression()
      model.fit(X, y)
      
      # Predict the stock prices for the given dataset
      predictions = model.predict(X)
      
      return predictions.tolist()