Data Visualization

Introduction to Data Visualization:

Data Visualization is the graphical representation of data in order to gain insights from complex datasets. It plays a crucial role in understanding the data by identifying correlations, patterns, and trends, aids in feature engineering, and more. It’s very useful in building an efficient model.

Examples include:

  • Scatter Plot: Used to visualise the relationship between two variables, such as input features and target outputs. They help identify correlations, outliers, and data clusters and facilitating feature selection.
  • Heatmap: Employed to evaluate the feature to feature and feature to target correlations, anomalies, patterns visually represented with colour grading.
  • Pie chart: Used to show the proportion of each category of a feature in slices comprising a whole.

Data Visualization charts are segregated into the following four categories:

Visualization types Brief use case
Composition charts Composition charts show how the parts of the data make up the whole
Distribution charts Distribution charts visually display the distribution or spread of categories in a group.
Comparison charts Comparison charts allow to compare two or more sets of data or variables.
Relationship charts Relationship charts visually illustrate the relationship between two or more variables and how they are correlated with each other.

Last Updated 2023-09-07 11:29:42 +0530 +0530