KNN Machine Learning Algorithm | KNN Algorithm Using Python | K Nearest Neighbor | Scikit-Learn

In this video we will cover how the K Nearest Neighbors (KNN) Machine Learning algorithm works. We will implement the KNN algorithm in python using sklearn library.
We are going to develop a KNN model and then use this model to make predictions. We will go over techniques of finding the optimal K value, evaluating the model and improving accuracy of predictions.
This KNN tutorial will help you understand what is KNN, why do we need to use KNN, and how KNN algorithm works.
You will also see a use case demo to predict whether a user will make a purchase or not using the KNN algorithm.

💻 To download data (data is under data folder) and Notebook go to:
Click on a green button to clone or download the entire repository and then go to relevant folder to get access to that specific file.

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🔗 Link to Exploratory Data Analysis with Python video:

🔗 Link to Multiple Linear Regression video with Label Encoder example:

🔗 Confusion Matrix Link (Tabular summary of predictions made by a classifier):

#MachineLearning #KNN #KNearestNeighbors

Topics covered in this KNN Machine Learning Video:
00:00 - 00:40 Introduction to KNN(K Nearest Neighbors)
00:40 - 01:00 What is KNN?
01:01 - 02:04 How does the KNN algorithm work?
02:05 - 02:49 Implement KNN in Python (Jupyter Notebook)
02:50 - 03:26 Import dataset
03:27 - 04:18 Label Encoding
04:19 - 04:40 Split data into train and test
04:41 - 04:40 Create and Train Model
05:36 - 06:05 Use case - Predict whether a person a purchase or not
06:06 - 06:56 Find optimal value of K
06:59 - 08:59 Evaluate the model
09:00 - 09:15 Looking Forward