input: 
  def simple_linear_regression():
      """
      This function performs simple linear regression on data loaded from a CSV file.
      It uses the linear_model module from sklearn to train a linear regression model.
      It then predicts the target variable and visualizes the regression line along with the data points.
      
      Example:
      >>> simple_linear_regression()
      # This will read data from 'data.csv', train the model, make predictions, and display the results graphically.
      """
  
output:     
      import pandas as pd
      from sklearn.linear_model import LinearRegression
      import matplotlib.pyplot as plt
  
      # Load the data from CSV
      data = pd.read_csv('data.csv')
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
  
      # Train the linear regression model
      model = LinearRegression()
      model.fit(X, y)
  
      # Make predictions
      y_pred = model.predict(X)
  
      # Visualize the results
      plt.scatter(X, y, color='blue', label='Data points')
      plt.plot(X, y_pred, color='red', label='Regression line')
      plt.xlabel('Feature')
      plt.ylabel('Target')
      plt.title('Simple Linear Regression')
      plt.legend()
      plt.show()
  