Difference between Classification and Clustering in Machine Learning

What is it?
Given a set of historical/old data along with their class name and a set of new data, classification is the process of assigning each new data with class name that is obtained from the old/historical data. 
Given a set of samples/data, clustering is the process of grouping the data based on their similarities and the patterns of the data.
Type of input data
Set of data with label/class name
Only the data set. No labeling is required.
  • A set of samples or data
  • A set of classes
A set of samples

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Classification using Naive Bayes Algorithm using Python

Data Classification mainly refers to a way of organizing/categorizing the data by assigning a label/class to a set of data. For example, from the age, we can the class Infants, children, adolescents, adult, or older adult to a person. It is very easy to classify if the data is very small or the feature set (here it is only the age of the person) is very small. What will happen, if the amount of data is very large? We can use different ML algorithms to perform the classification task on a large dataset with hundreds of the features. In this post, we will learn how to classify a set of data using the popular supervised Naive Bayes algorithm using Python language. Continue reading…