**2.1 Supervised learning**

Supervised Learning as the name indicates it means "having a mentor" to supervise, which means that the output is already present and the input gives the output accordingly. It contains a model that is used to predict the outcomes with the help of labelled dataset. The learned relationship between the input, output and the parameters of the system is learned by system model. The training is given to this model and once it is complete the model is tested on the basis of test data and then it predicts the output with the help of labelled dataset (it is a dataset wherein the target answer is already known) (**Figure 9**) [12].

It also finds the mapping function to map the input variable and the output variable. This type of approach is used to solve diverse issues for WSN such as object targeting and localization, processing of query and event detection, medium access control, intrusion detection and security, image classification, spam filtering, data integration and security.

**Figure 9.**

```
Supervised learning model.
```
Guided learning involves the following steps:


The supervised learning can be further divided into two categories: *Regression* and *Classification.*
