This is the code for the "How to Do Linear Regression the Right Way" live session by Siraj Raval on Youtube
##Overview
This is the code for this video on Youtube by Siraj Raval. I'm using a small dataset of student test scores and the amount of hours they studied. Intuitively, there must be a relationship right? The more you study, the better your test scores should be. We're going to use linear regression to prove this relationship.
Here are some helpful links:
####Gradient descent visualization https://raw.githubusercontent.com/mattnedrich/GradientDescentExample/master/gradient_descent_example.gif
####Sum of squared distances formula (to calculate our error) https://spin.atomicobject.com/wp-content/uploads/linear_regression_error1.png
####Partial derivative with respect to b and m (to perform gradient descent) https://spin.atomicobject.com/wp-content/uploads/linear_regression_gradient1.png
##Dependencies
- numpy
Python 2 and 3 both work for this. Use pip to install any dependencies.
##Usage
Just run python3 demo.py to see the results:
Starting gradient descent at b = 0, m = 0, error = 5565.107834483211
Running...
After 1000 iterations b = 0.08893651993741346, m = 1.4777440851894448, error = 112.61481011613473
##Credits
Credits for this code go to mattnedrich. I've merely created a wrapper to get people started.