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.
Outliers are the data that are distant away from the all other observation or unusual data that doesn't fit the data. In other words, outliers are the data that does not fit the mainstream of data.
Categorical features refers to string type data and can be easily understood by human beings. But in case of machine, it cannot interpret the categorical data directly. Therefore, the categorical data must be translated into numerical data that can be understood by machine.
Linear Regression is a machine learning model that is based on supervised learning. It performs regression task. This model maps the linear relationship between dependent and independent variables, so have name linear regression.