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
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