Explain the difference between classification, regression, and clustering in machine learning.

Classification is a supervised machine learning method where the model tries to predict the correct label of a given input data. In classification, the model is fully trained using the training data, and then it is evaluated on test data before being used to perform prediction on new unseen data. for example :

classification can be of multiple types:

• Binary Classification
• Multi-Class Classification
• Multi-Label Classification

Regression :

Regression is a machine Learning (ML) model with is use to find the relation between dependent and independent variables and the regression model deals with the numerical data.  Regression model can be used for prediction of continuous values such as house value prediction, salary prediction etc.
regression model is mainly divided into two types which are been used frequently :

• Linear Regression
• Logistic Regression

Clustering :

clustering is a Machine Learning (ML) method which is use to group the datapoints having similar features or patterns.  It help the system to identify hidden groups without predefine labels.  When the new data is enter it also states where they may belong to and make the prediction according to it.
clustering can be of different types which are:

• K-Means Clustering
• Hierarchical Clustering
• Fuzzy Clustering