## Supervised Learning

Supervised learning is a learning system that trains using labeled data (data in which the target variables are already known). The model learns how patterns in the feature matrix map to the target variables. When the trained machine is fed with a new dataset, it can use what it has learned to predict the target variables. This can also be called predictive modeling.

Supervised learning is broadly split into two categories. These categories are as follows:

**Classification** mainly deals with categorical target variables. A classification algorithm helps to predict which group or class a data point belongs to.

When the prediction is between two classes, it is known as binary classification. An example is predicting whether or not a customer will buy a product (in this case, the classes are yes and no).

If the prediction involves more than two target classes, it is known as multi-classification; for example, predicting all the items that a customer will buy.

**Regression** deals with numerical target variables. A regression algorithm predicts the numerical value of the target variable based on the training dataset.

Linear regression measures the link between one or more predictor variables and one outcome variable. For example, linear regression could help to enumerate the relative impacts of age, gender, and diet (the predictor variables) on height (the outcome variable).

**Time series analysis**, as the name suggests, deals with data that is distributed with respect to time, that is, data that is in a chronological order. Stock market prediction and customer churn prediction are two examples of time series data. Depending on the requirement or the necessities, time series analysis can be either a regression or classification task.