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Clustering Demystified
course content

Course Content

Clustering Demystified

Clustering Demystified

IntroductionImport Necessary Libraries and DataPreliminary AnalysisData PreparationDeclare Feature Vector and Target VariableConvert Categorical Variable into IntegersFeature ScalingK-Means Model with 2 ClustersCheck the QualityHow many Clusters?K-Means Model with 4 Clusters

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Declare 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.

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.

Task

Swipe to start coding

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

Solution

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SectionΒ 1. ChapterΒ 5

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course content

Course Content

Clustering Demystified

Clustering Demystified

IntroductionImport Necessary Libraries and DataPreliminary AnalysisData PreparationDeclare Feature Vector and Target VariableConvert Categorical Variable into IntegersFeature ScalingK-Means Model with 2 ClustersCheck the QualityHow many Clusters?K-Means Model with 4 Clusters

book
Declare 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.

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.

Task

Swipe to start coding

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

Solution

Mark tasks as Completed
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 1. ChapterΒ 5
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