Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Preliminary Analysis | Clustering Demystified
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

bookPreliminary Analysis

Preliminary analysis involves initial exploration and understanding of data to identify patterns, trends, or anomalies. It serves as a foundation for more in-depth analysis and decision-making in various domains such as business, research, and data science.

Methods description

  • print: This is a built-in Python function used to display the value of an expression. It prints the specified message or variable to the standard output (usually the console);
  • shape: This is a method available in data structures like Pandas DataFrame or NumPy array. It returns a tuple representing the dimensions of the data structure, often in the format (rows, columns). In this context, it prints the shape of the data, i.e., the number of rows and columns;
  • isnull(): This is a method available in Pandas DataFrame which returns a boolean DataFrame indicating whether each element in the DataFrame is NaN (missing) or not;
  • sum(): This is a method available in Pandas DataFrame which returns the sum of values for the requested axis. When used after isnull(), it computes the sum of missing values along the specified axis (usually axis=0 for columns). In this context, it prints the total number of missing values in each column.

Task

  1. Print the shape of your data.
  2. Check for any NaN value.

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!

Preliminary analysis involves initial exploration and understanding of data to identify patterns, trends, or anomalies. It serves as a foundation for more in-depth analysis and decision-making in various domains such as business, research, and data science.

Methods description

  • print: This is a built-in Python function used to display the value of an expression. It prints the specified message or variable to the standard output (usually the console);
  • shape: This is a method available in data structures like Pandas DataFrame or NumPy array. It returns a tuple representing the dimensions of the data structure, often in the format (rows, columns). In this context, it prints the shape of the data, i.e., the number of rows and columns;
  • isnull(): This is a method available in Pandas DataFrame which returns a boolean DataFrame indicating whether each element in the DataFrame is NaN (missing) or not;
  • sum(): This is a method available in Pandas DataFrame which returns the sum of values for the requested axis. When used after isnull(), it computes the sum of missing values along the specified axis (usually axis=0 for columns). In this context, it prints the total number of missing values in each column.

Task

  1. Print the shape of your data.
  2. Check for any NaN value.

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 3
AVAILABLE TO ULTIMATE ONLY
some-alt