I had some fun tonight writing a linear regression...
# datascience
I had some fun tonight writing a linear regression algorithm from scratch in Kotlin. To execute gradient descent, I spent an hour figuring out partial derivatives, which basically is a Calculus derivative targeting one variable at a time, assuming all the other variables are constant. The partial derivatives can then be used to measure slope of the error, and when that slope approaches 0 you hit a local minimum and found an optimal line. I'll post a video on this later. [Source Code is on GitHub](https://gist.github.com/thomasnield/fbe2e2205233388577e6abe6f5bbe897) [Desmos Graph](https://www.desmos.com/calculator/ntomwigo6k)
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