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`. 

In this project, we are going to understand what a cluster is and how to use it in Python.

Clustering Demystified

Declare Feature Vector and Target Variable

Methods description

Solution