A feature vector is a set of numerical features that represent an object or sample. In machine learning, a feature vector is used as input to a model, and it typically contains multiple features that describe the characteristics of the object.

A target variable, also known as a response or dependent variable, is the variable that the model is trying to predict. It is the output of the model, and it is typically a numerical or categorical value.

For example, in a supervised learning problem where we want to predict the price of a house, the feature vector might include things like the number of bedrooms, square footage, and neighborhood, while the target variable would be the price of the house.

Methods description

The indexing [] operator is used to select specific columns from the DataFrame. For example, data[column_name] retrieves the column named "column_name" from the DataFrame data.