Hyperparameter tuning is an essential term in the field of machine learning. Hyperparameter is a set of parameters that control the learning process of the algorithm.
Feature scaling is one of the important step in data pre-processing. Scaling refers to converting the original form of data to another form of data within a certain range. Many machine learning models perform well when the input data are scaled to the standard range
Null values in dataset are the empty field represented as NaN(Not a Number). Null value do not mean zero value, actually it is an empty field. Datasets that are available for preparing machine learning model may contain some null values in it.
Feature selection is the process of reducing number of input features when developing a machine learning model. It is done because it reduces the computational cost of the model and to improve the performance of the model. Features that have high correlation with output variable is selected for training the model.