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Clustering Demystified
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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

bookImport Necessary Libraries and Data

Firstly, we need to import all necessary modules, upload the data, and visually examine it.

Modules and methods description

  • numpy (np): Essential for numerical computing and array operations;
  • pandas (pd):
    • read_csv(): Facilitates reading CSV files into DataFrame for data analysis;
    • DataFrame.head(): Offers a quick glimpse of the initial rows in the DataFrame.
  • matplotlib.pyplot (plt): Enables creating various types of plots for data visualization;
  • seaborn (sns): Enhances statistical data visualization based on matplotlib.

Task

  1. Import numpy, pandas, matplotlib.pyplot and seaborn (as np, pd, plt, and sns, respectively).
  2. Read the "Live.csv" file.
  3. Show the first 5 rows of the dataset.

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Firstly, we need to import all necessary modules, upload the data, and visually examine it.

Modules and methods description

  • numpy (np): Essential for numerical computing and array operations;
  • pandas (pd):
    • read_csv(): Facilitates reading CSV files into DataFrame for data analysis;
    • DataFrame.head(): Offers a quick glimpse of the initial rows in the DataFrame.
  • matplotlib.pyplot (plt): Enables creating various types of plots for data visualization;
  • seaborn (sns): Enhances statistical data visualization based on matplotlib.

Task

  1. Import numpy, pandas, matplotlib.pyplot and seaborn (as np, pd, plt, and sns, respectively).
  2. Read the "Live.csv" file.
  3. Show the first 5 rows of the dataset.

Mark tasks as Completed
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 2
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