K Nearest Neighbors Algorithm using Python From Absolute Scratch [No Scikit Learn]

In our previous videos, we introduced Classification, which is a supervised form of machine learning, and explained the intuition for K Nearest Neighbors algorithm.
We are going to apply the entire algorithm on a real world datset using Python From Absolute Scratch [No Sciit Learn]
In this tutorial, we are actually going to apply the KNN Algorithm on a real world dataset. We will first see how splitting of entire dataset is done, we will also see how we can compute the Euclidean Distance and how we can use these metrics in our application of the algorithm to get the results. In a later video, we will also see how we can apply Scikit-Learn and create classifiers such as K Nearest Neighbors, Naive Bayes using it. In this video we are going to build our own approach in form of a project and then understand how everything works under the hood in KNN Algorithm.
To exemplify classification, we're going to use a Iris Dataset to classify the flowers into their right categories which is a dataset donated to the University of California, Irvine (UCI) collection from the University of Wisconsin-Madison. UCI has a large Machine Learning Repository.
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[TheGustavoGamerBR]

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