Learn Preliminary Analysis | Clustering Demystified

Course Content

Clustering Demystified

Introduction Import Necessary Libraries and Data Preliminary Analysis Data Preparation Declare Feature Vector and Target Variable Convert Categorical Variable into Integers Feature Scaling K-Means Model with 2 Clusters Check the Quality How many Clusters?K-Means Model with 4 Clusters

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

Swipe to start coding

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

Solution

Mark tasks as Completed

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 1. Chapter 3

AVAILABLE TO ULTIMATE ONLY