input: Write a python function to model simple linear regression in Boston housing data and plot the regression line.
  assert len(model_simple_linear_regression().coef_)==1
  assert model_simple_linear_regression().intercept_ is not None
output: def model_simple_linear_regression():
      import numpy as np
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LinearRegression
      import matplotlib.pyplot as plt
      from sklearn.datasets import load_boston
      
      # Load the Boston housing dataset
      boston = load_boston()
      df = pd.DataFrame(boston.data, columns=boston.feature_names)
      df['MEDV'] = boston.target
      
      # Select the feature and the target
      X = df[['RM']]  # average number of rooms per dwelling
      y = df['MEDV']  # median value of owner-occupied homes in $1000's
      
      # Split the data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Create and train the linear regression model
      lr = LinearRegression()
      lr.fit(X_train, y_train)
      
      # Plot the regression line
      plt.scatter(X_train, y_train, color='blue', label='Training data')
      plt.plot(X_train, lr.predict(X_train), color='red', linewidth=2, label='Regression line')
      plt.xlabel('Average number of rooms per dwelling (RM)')
      plt.ylabel('Median value of owner-occupied homes (MEDV)')
      plt.title('Simple Linear Regression')
      plt.legend()
      plt.show()
      
      return lr