https://github.com/siddhant4srivastava/numeric-and-visual-summary
Exploring Data with Numeric and Visual Summaries of a Bank Loan Dataset
https://github.com/siddhant4srivastava/numeric-and-visual-summary
data-science data-visualization
Last synced: 8 months ago
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Exploring Data with Numeric and Visual Summaries of a Bank Loan Dataset
- Host: GitHub
- URL: https://github.com/siddhant4srivastava/numeric-and-visual-summary
- Owner: siddhant4srivastava
- Created: 2024-08-25T16:01:45.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-25T16:06:36.000Z (almost 2 years ago)
- Last Synced: 2024-08-25T17:24:00.303Z (almost 2 years ago)
- Topics: data-science, data-visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 1.48 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Numeric-and-Visual-Summary
Exploring Data with Numeric and Visual Summaries of a Bank Loan Dataset
Data visualization is a powerful tool that helps us understand complex datasets in an intuitive way. In our latest exploration, we dive deep into a bank loan dataset, showcasing how to effectively visualize and interpret data.
Types of Visualizations:
1. Single Numeric Variables: Discover the distribution of variables using density plots and identify outliers with box plots.
2. Numeric vs. Numeric Variables: Uncover relationships between pairs of numeric variables through scatter plots and pairwise density plots.
3. Faceting Data: Learn how to visualize data chunks based on categorical variables for deeper insights.
4. Categorical Variables: Get a clear view of categorical data with frequency bar plots.
5. Faceting of Categorical Variables: Dive into segmented views of your categorical data.
6. Heatmaps: Visualize correlations and patterns in matrix form using heatmaps.