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K Means Clustering Mathematics & Elbow Method to find optimal value of K | Data Science | Machine Learning | Explanation | K means clustering(KMC) is an unsupervised machine learning algorithm. This directly means the supervision is not here to help the model to learn. In KMC the all process of classification of data is done by the model itself, it recognises the features of the data points and then with more likely features data it put them all in a group called clusters . Basically, it makes the multiple clusters of the related data by identifying the features in the dataset. Credits: giphy.com It is an unsupervised machine learning algorithm, which use to classify the data into cluster form. Each cluster contains similar type of data points. For example, you have some apple, orange and banana then you have to classify them then if you feed it to KMC. KMC will make a group of fruits which looks like same like banana are long and yellow. Orange is spherical and the colour is orang...

It is a classification algorithm, which is extremely fast than other algorithms. If you are stuck with the large dataset then go with it. It will do it very effectively. Actually, it is a collection of the classifier algorithm. It is a family of algorithms in which every single algorithm assumes that the presence of one feature is independent of the presence of other features. Photo by Crissy Jarvis on Unsplash But also the assumption assumed by the Naive Algo is not correct in the real world, so we can also get undesired results. Because it assumes every features independent of each other but there may be a case that the features are directly dependent on each other. But still, it works well in practice and we use it. Bayes Theorem: Bayes theorem is used to find the probability of an event occurring given that another event already occurred. We can apply Bayes theorem like this... Here,      c = the target class     x = dependent feature vector of size n c = {...