input: Write a python function to read a CSV file, fit a linear regression model, and make predictions, including a visualization of the results.
  assert len(linear_regression_prediction("stocks.csv", 10)) == 10
  assert linear_regression_prediction("stocks.csv", 1)[0] > 0
  assert isinstance(linear_regression_prediction("stocks.csv", 5), list)
output: def linear_regression_prediction(file_path, num_predictions):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LinearRegression
      import matplotlib.pyplot as plt
  
      # Read CSV file
      data = pd.read_csv(file_path)
      
      # Assuming the CSV file has columns 'Feature' and 'Target'
      X = data[['Feature']]
      y = data['Target']
      
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Fit the linear regression model
      model = LinearRegression()
      model.fit(X_train, y_train)
      
      # Make predictions
      predictions = model.predict(X_test)
      
      # Visualization
      plt.scatter(X_test, y_test, color='black', label='Actual Data')
      plt.plot(X_test, predictions, color='blue', linewidth=3, label='Linear Regression')
      plt.xlabel('Feature')
      plt.ylabel('Target')
      plt.title('Linear Regression Prediction')
      plt.legend()
      plt.show()
      
      # Generate future predictions
      future_features = pd.DataFrame({'Feature': range(len(X), len(X) + num_predictions)})
      future_predictions = model.predict(future_features)
      
      return future_predictions.tolist()