As a data scientist, you may very frequently encounter categorical variable in your dataset like location, car model, gender, etc. You cannot directly use them in our machine learning algorithm as these algorithms only understand numbers. There are various techniques to convert these categorical features to numerical features but that is not the focus of this post, this post is about how to implement these techniques in python. I will talk a little bit about these techniques but won’t go into too much depth, I will emphasise more on various ways how you can implement this technique in python.

K-Nearest Neighbour is the simplest of machine learning algorithms which can be very effective in some cases. The objective of the post it to implement it from scratch in python, you need to know a fair bit of python for and a little bit of numpy for the faster version of the algorithm. Once we have implemented the algorithm we will also see how to improve the performance of the algorithm. As there is no single invincible algorithm, we will look into advantage/disadvantage of the algorithm, this will help us to decide on when to use the algorithm. Alright, then let’s get straight into it.