This is a little write up of a very small project I did, inspired by Coding Garden with CJ on youtube & twitch (specifically this video), and Net Ninja express tutorials:
A URL-shortener that uses a sequence of emojis to encode each URL.
The code is available on github, and you can try it out at emoj.yt.
Today I want to explain linear regression. It is one of the simplest statistical learning models and can be implemented in only a couple lines of Python code, in an efficient manner. Being so simple however does not mean it is not useful, in fact it can be very practical to explore relationships between features in a dataset and make predictions on a target value. Therefore I think it’s important to understand how the method works and how the different parameters have an effect on the outcome.
This is a little writeup of a project I did in collaboration with a classmate while studying a algorithmic complexity class. We implemented a faster, but still exact, \(k\) nearest neighbors classifier based on k-d trees. I learned a lot and hope this can be interesting to some of you.
This is Part 3 of my decision trees series. This time around we are going to code a decision tree in Python. So I’m going to try to make this code as understandable as possible, but if you are not familiar with Object Oriented Programming (OOP) or recursion you might have a tougher time.