Feature represents the column or attribute of the data used for analysis such as the Number of rooms, size of the house, etc. Feature and variable are interchangeable words in Machine learning many times but there is a basic difference. The variables after transformations on variables are called features. Suppose, X is a variable in data, you derived a new variable X2 and this X2 is a feature. The selection of features for building a model is very important because including all the features may lead to overfitting. There are certain techniques to choose features for models.
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You can watch this video on Machine learning Full course to know about feature, feature scaling, and feature selection techniques: