A simple, yet powerful algorithm for classification and regression, based on proximity to the closest data points
Nearest Neighbour (K-NN) Overview
K-NN is one of the simplest Machine Learning algorithms based on the Supervised Learning technique.
The algorithm assumes the similarity between the new case/data and available cases and categorizes the new
case into the most similar category.
K-NN stores all available data and classifies a new data point based on similarity. When new data appears,
it can be easily classified into a good-suited category using the K-NN algorithm.
K-NN can be used for both Regression and Classification, but it is mostly used for Classification problems.
K-NN is a non-parametric algorithm, meaning it does not make any assumptions about the underlying data.
It is called a lazy learner algorithm because it does not learn from the training set immediately; instead,
it stores the dataset and performs actions on it during classification.
During the training phase, K-NN just stores the dataset. When new data is introduced, it classifies the data
into a category that is most similar to the new data.
Example
Suppose we have an image of a creature that looks similar to both a cat and a dog, and we want to know
whether it is a cat or dog. The K-NN algorithm can help with this identification based on a similarity
measure. The model will find the similar features of the new dataset to the images of cats and dogs, and based
on the most similar features, it will categorize it as either a cat or a dog.
Data Pre-Processing Step
Lesson 2: Why do we need a K-NN Algorithm?
Lesson 3: How does K-NN work?
Lesson 4: How to select the value of K in the K-NN Algorithm?
Lesson 5: Advantages and Disadvantages of KNN Algorithm
Quiz
Chapter 2: Python implementation of the KNN algorithm
Lesson 6: Steps to implement the K-NN algorithm
Lesson 7: Data Pre-Processing Step
Lesson 8: Fitting K-NN classifier to the Training data
Lesson 9: Creating the Confusion Matrix
Lesson 10: Visualizing the Training set result & output
Quiz
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This is to express our sincere appreciation to ITnurtureDen
for their valuable counselling sessions on well-being and
career guidance for MBA students in our training program.
There has been a phenomenal improvement in the students'
overall well-being and their performance in various
interviews. We are happy to have them associated with us and
look forward to a long-term relationship.
This is to express our sincere appreciation to ITnurtureDen
for their valuable counselling sessions on well-being and
career guidance for MBA students in our training program.
There has been a phenomenal improvement in the students'
overall well-being and their performance in various
interviews. We are happy to have them associated with us and
look forward to a long-term relationship.
