title: Handling unassigned (null) values of features in regression (machine learning)?
body: <p>I want to do linear regression analysis. I have multiple features. Some features has unassigned (null) values for some items in data. Because for some items some specific feature values were missed in data source. To be more clear, I provide example:
  <a href="https://i.stack.imgur.com/s2wo8.jpg" rel="noreferrer"><img src="https://i.stack.imgur.com/s2wo8.jpg" alt="enter image description here"></a> </p>
  
  <p>As you can see, some items missing values for some features. For now, I just assigned it to 'Null', but how to handle this values when doing linear regression analysis of the data? I do not want this unassigned values to incorrectly affect regression model. Unfortunately I cannot get rid of items where unassigned feature values presented. I plan to use Python for regression. </p>