input: Write a python function to implement a linear regression model to predict stock prices. The function should read data from a CSV file, split the data into training and testing sets, train the model, make predictions, and print the predicted and actual prices. Use the given code as a guide.
  
  ```python
  import pandas as pd
  import numpy as np
  from sklearn.linear_model import LinearRegression
  
  def predict_stock_prices(file_path):
      # Read in csv data
      data = pd.read_csv(file_path)
  
      # Split our data into testing and training sets
      X_train = data[data['date'] < '2014-01-01']
      X_test = data[data['date'] >= '2014-01-01']
  
      # Extract our feature and target columns
      X_train = X_train.drop(['date', 'price'], axis=1)
      X_test = X_test.drop(['date', 'price'], axis=1)
      y_train = data[data['date'] < '2014-01-01']['price']
      y_test = data[data['date'] >= '2014-01-01']['price']
  
      # Fit our Linear Regression Model
      linear_regression = LinearRegression()
      linear_regression.fit(X_train, y_train)
      y_preds = linear_regression.predict(X_test)
  
      # Print out our predictions
      for i in range(1, len(y_preds)):
          print(f"Predicted: {y_preds[i]}, Actual: {y_test.iloc[i]}")
  
  # Example usage with assertions
  predict_stock_prices('stock_data.csv')
  
  # Note: You will need to replace the file_path with an actual CSV file path and have pandas, numpy, and sklearn installed.
output: from sklearn.model_selection import train_test_split
  
  def predict_stock_prices(file_path):
      # Read in csv data
      data = pd.read_csv(file_path)
  
      # Convert the 'date' column to datetime
      data['date'] = pd.to_datetime(data['date'])
  
      # Extract our feature and target columns
      X = data.drop(['date', 'price'], axis=1)
      y = data['price']
  
      # Split our data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)
  
      # Fit our Linear Regression Model
      linear_regression = LinearRegression()
      linear_regression.fit(X_train, y_train)
      y_preds = linear_regression.predict(X_test)
  
      # Print out our predictions
      for i in range(len(y_preds)):
          print(f"Predicted: {y_preds[i]}, Actual: {y_test.iloc[i]}")
  
  # Example usage with assertions
  predict_stock_prices('stock_data.csv')
  
  # Note: You will need to replace the file_path with an actual CSV file path and have pandas, numpy, and sklearn installed.