# Prediction target and Features in Machine Learning

## **Prediction Target**

* It represents the output of an ML model.
    
* For example, In a supervised learning algorithm, if you are developing a model for predicting house prices then your dataset will have a “Price” column, so this column will be your prediction target.• Only one column can be the prediction target.
    
* Represented as “y”.
    
* It can be accessed with a dot notation approach as follows.y = my\_dataset.Price
    

## **Features**

* Features are nothing but the inputs that are provided to a model.
    
* The feature can be any number of columns from the dataset, except for the prediction target.
    
* Based on the selected input columns (features) the output will be predicted by the model.
    
* Represented as “X” as follows.
    

```markdown
features = [‘TotalRooms’, ‘Floors’, ‘Landsize’]X = my_dataset[melbourne_features]]
```