# Reggie's Linear Regression Project (help?)

Hi
I just completed the “Reggie’s Linear Regression” project as a part of my Python Fundamentals for Data Science (Part II) course. I am brand new to Python and coding in general and would appreciate a review/some help with my code!
Was feeling pretty confident until I hit Part 12 of the project. I noticed the value my code returned for “smallest_error” and it’s corresponding m and b values differed from Codecademy’s but I’m not sure where I went wrong as I was getting the correct answers up until that point.

Project Link

Link to my code

(I’m also new to GitHub so I hope that link worked.)

Thanks in advance for any insight!

Hello! I’m also taking the Data Science course and a beginner here as well.
I noticed your code is using a different datapoints list from the one that is used by the project, which is indicated part 1, task 4. I think that’s why you are getting different values.

`datapoints = [(1, 2), (2, 0), (3, 4), (4, 4), (5, 3)]`

I also think you could simplify your code by removing the function line y = () as it isn’t necessary to have it explicit and the formula is pretty clear in itself. You could even try to simplify it even more by returning the result of the expression directly.

`return (m*x) + b`

The get_y function could be used inside your calculate_error to make it useful and to avoid repeating the formula.