KNN Algorithm In Machine Learning | KNN Algorithm Using Python | K Nearest Neighbor | Simplilearn
This KNN Algorithm in Machine Learning tutorial will help you understand what is KNN, why do we need KNN, and how KNN algorithm works. You will learn how do we choose the factor 'K', when do we use KNN, and you will also see a use case demo to predict whether a person will have diabetes or not using the KNN algorithm.
Below topics are explained in this K-Nearest Neighbor Algorithm (KNN Algorithm) tutorial:
00:00 - 00:57 Introduction to KNN(K Nearest Neighbor)
00:57 - 02:33 Why do we need KNN?
02:33 - 03:51 What is KNN?
03:51 - 05:46 How do we choose the factor 'K'?
05:46 - 06:42 When do we use KNN?
06:42 - 09:19 How does the KNN algorithm work?
09:19 - 27:42 Use case - Predict whether a person will have diabetes or not
🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=KNNInMLMachineLearning&utm_medium=Description&utm_source=youtube
Dataset Link - https://drive.google.com/drive/folders/1YylTjWxmkUVdurMSDjl84dstCKZL6wH8
✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH
⏩ Check out the Machine Learning tutorial videos: https://bit.ly/3fFR4f4
You can also go through the slides here: https://goo.gl/XP6xcp
#KNNAlgorithmInMachineLearning #KNNAlgorithm #KNN #KNearestNeighbor #KNNMachineLearning #KNNAlgorithmPython #KNearestNegighborMachineLearning #MachineLearningAlgorithm #MachineLearning #Simplilearn
What is KNN?
K-Nearest Neighbors is one of the simplest supervised machine learning algorithms used for classification. It classifies a data point based on its neighbors’ classifications. It stores all available cases and classifies new cases based on similar features.
When Do We Use the KNN Algorithm?
The KNN algorithm is used in the following scenarios:
✅Data is labeled
✅Data is noise-free
✅Dataset is small, as KNN is a lazy learner
Pros and Cons of Using KNN
✅Pros: Since the KNN algorithm requires no training before making predictions, new data can be added seamlessly, which will not impact the accuracy of the algorithm.
KNN is very easy to implement. There are only two parameters required to implement KNN—the value of K and the distance function (e.g. Euclidean, Manhattan, etc.)
✅Cons: The KNN algorithm does not work well with large datasets. The cost of calculating the distance between the new point and each existing point is huge, which degrades performance.
Feature scaling (standardization and normalization) is required before applying the KNN algorithm to any dataset. Otherwise, KNN may generate wrong predictions.
✅KNN Algorithm Uses in Real World
In the real world, the KNN algorithm has applications for both classification and regression problems. KNN is widely used in almost all industries, such as healthcare, financial services, eCommerce, political campaigns, etc. Healthcare companies use the KNN algorithm to determine if a patient is susceptible to certain diseases and conditions. Financial institutions predict credit card ratings or qualify loan applications and the likelihood of default with the help of the KNN algorithm.
To learn more about KNN, check out Machine Learning course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=KNNInMachineLearning&utm_medium=Description&utm_source=youtube
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Master the concepts of supervised, unsupervised, and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering, and more.
5. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
This KNN Algorithm in Machine Learning tutorial will help you understand what is KNN, why do we need KNN, and how KNN algorithm works. You will learn how do we choose the factor ‘K’, when do we use KNN, and you will also see a use case demo to predict whether a person will have diabetes or not using the KNN algorithm.
Below topics are explained in this K-Nearest Neighbor Algorithm (KNN Algorithm) tutorial:
00:00 – 00:57 Introduction to KNN(K Nearest Neighbor)
00:57 – 02:33 Why do we need KNN?
02:33 – 03:51 What is KNN?
03:51 – 05:46 How do we choose the factor ‘K’?
05:46 – 06:42 When do we use KNN?
06:42 – 09:19 How does the KNN algorithm work?
09:19 – 27:42 Use case – Predict whether a person will have diabetes or not
🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=KNNInMLMachineLearning&utm_medium=Description&utm_source=youtube
Dataset Link – https://drive.google.com/drive/folders/1YylTjWxmkUVdurMSDjl84dstCKZL6wH8
✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH
⏩ Check out the Machine Learning tutorial videos: https://bit.ly/3fFR4f4
You can also go through the slides here: https://goo.gl/XP6xcp
#KNNAlgorithmInMachineLearning #KNNAlgorithm #KNN #KNearestNeighbor #KNNMachineLearning #KNNAlgorithmPython #KNearestNegighborMachineLearning #MachineLearningAlgorithm #MachineLearning #Simplilearn
What is KNN?
K-Nearest Neighbors is one of the simplest supervised machine learning algorithms used for classification. It classifies a data point based on its neighbors’ classifications. It stores all available cases and classifies new cases based on similar features.
When Do We Use the KNN Algorithm?
The KNN algorithm is used in the following scenarios:
✅Data is labeled
✅Data is noise-free
✅Dataset is small, as KNN is a lazy learner
Pros and Cons of Using KNN
✅Pros: Since the KNN algorithm requires no training before making predictions, new data can be added seamlessly, which will not impact the accuracy of the algorithm.
KNN is very easy to implement. There are only two parameters required to implement KNN—the value of K and the distance function (e.g. Euclidean, Manhattan, etc.)
✅Cons: The KNN algorithm does not work well with large datasets. The cost of calculating the distance between the new point and each existing point is huge, which degrades performance.
Feature scaling (standardization and normalization) is required before applying the KNN algorithm to any dataset. Otherwise, KNN may generate wrong predictions.
✅KNN Algorithm Uses in Real World
In the real world, the KNN algorithm has applications for both classification and regression problems. KNN is widely used in almost all industries, such as healthcare, financial services, eCommerce, political campaigns, etc. Healthcare companies use the KNN algorithm to determine if a patient is susceptible to certain diseases and conditions. Financial institutions predict credit card ratings or qualify loan applications and the likelihood of default with the help of the KNN algorithm.
To learn more about KNN, check out Machine Learning course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=KNNInMachineLearning&utm_medium=Description&utm_source=youtube
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Master the concepts of supervised, unsupervised, and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering, and more.
5. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
