General Description: In this video, we begin by showcasing how to build an iris classification model, that is, a machine learning model that will allow us to classify species of iris flowers. This application will introduce many rudimentary features and concepts of machine learning and is a good use case for these types of models. Use case: Botanist wants to determine the species of an iris flower based on characteristics of that flower. For instance attributes including petal length, width, etc. are the "features" that determine the classification of a given iris flower. Part 3 Description: We use sklearn to invoke the K-nearest neighbors algorithm to determine whether a given sample is of a specific species of iris. This video is part of a series on Machine Learning in Python. The link to the playlist may be accessed here: http://bit.ly/lp_mlearn Python Code: Part 1: https://github.com/vprusso/youtube_tutorials/blob/master/machine_learning/iris_classification/part_1.py Part 2: https://github.com/vprusso/youtube_tutorials/blob/master/machine_learning/iris_classification/part_2.py Part 3: https://github.com/vprusso/youtube_tutorials/blob/master/machine_learning/iris_classification/part_3.py If I've helped you, feel free to buy me a beer :) PayPal: https://www.paypal.me/VincentRusso1 Do you like the development environment I'm using in this video? It's a customized version of vim that's enhanced for Python development. If you want to see how I set up my vim, I have a series on this here: http://bit.ly/lp_vim If you've found this video helpful and want to stay up-to-date with the latest videos posted on this channel, please subscribe: http://bit.ly/lp_subscribe