KNN Classification In Machine Learning | K-Nearest Neighbor Algorithm Explained | Simplilearn

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In this video on KNN Classification Tutorial, we dive into K Nearest Neighbors (KNN), one of the most popular algorithms in data science. You’ll learn how Python makes it easy to use KNN for classifying data based on its closest neighbors. Widely applied in fields like healthcare, finance, and agriculture, KNN helps us make decisions using data insights. We’ll also cover the challenges of KNN, such as its limitations with large datasets and the importance of proper data scaling. Using the Iris dataset, we’ll demonstrate step-by-step Python code to find the best ‘K’ value and maximize model accuracy. Don’t miss this hands-on tutorial—watch now to see KNN in action!

Dataset Link – https://drive.google.com/file/d/1pRz7wzQq2tEZvjPMQnxZK9Sl4MvOX9QA/view?usp=sharing

00:00 Introduction to KNN Algorithms
01:43 KNN Classification Demo
03:37 Loading the dataset
06:57 Dividing data into features and labels
09:14 Splitting dataset into training set and test set
11:00 Data Visualizations
12:05 Parallel Coordinates
15:55 Andrews Curves
18:53 Pairplot
21:03 Boxplots
22:15 Using KNN for classification
25:48 Calculating model accuracy
30:03 Finding best k

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#KNN #KNearestNeighbors #ArtificialIntelligence #MachineLeaerning #simplilearn #2025

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