In this video I'll show you how to train and evaluate a K Nearest Neighbor (KNN) Classifier using Python and the Scikit-learn library. We essentially do the same things we implemented from scratch during this course, using Python and sklearn in the way people typically do when doing Machine Learning.
The Course: [ Ссылка ]
⭐️HOMEWORK⭐️
Install matplotlib and use it to display the feature values. Customize it to look the way you want and show me what you come up with! Can you make it display the decision boundary as well? (Hint: look inside the sklearn.inspection module). Share your code on my Discord:
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I'll showcase my favorites in a future video.
⭐️LINKS⭐️
📁 Data
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💻 Code
tps://github.com/gniziemazity/ml-course
✔️ Use P8 to follow along
✔️ P9 is the code after this lesson
Python download link:
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How to setup VS Code to use Python:
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Scikit-learn library:
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⭐️TIMESTAMPS⭐️
00:00 Introduction
00:48 Converting the data into CSV
04:27 Reading a file using Python
05:35 Separating the feature values from the labels
08:52 K Nearest Neighbor using Scikit-learn
10:39 Reading the testing data and evaluating the classifier
11:27 Homework (Python)
