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Python Clustering Demystified: Exploring Data Groups

1. Clustering

IntroductionImport Necessary Libraries and DataPreliminary AnalysisData PreparationDeclare Feature Vector and Target VariableConvert Categorical Variable into IntegersFeature ScalingK-Means Model with Two ClustersCheck the QualityHow many Clusters?K-Means Model with 4 Clusters

Python Clustering Demystified: Exploring Data Groups

Declare Feature Vector and Target VariableDeclare Feature Vector and Target Variable

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.

The task is completed!

TaskCompleted

  1. Declare feature vector (entire data);
  2. Declare the target variable ("status_type" column).

Everything was clear?

Section 1. Chapter 5