Analyze categorical features vs. income distribution
Open
good first issue
track: exploration
very easy
- Dominant language
- Jupyter Notebook
- Stars
- 0
- Forks
- 16
- PR merge metrics
- No merged PRs in 30d
Description
Choose 3 to 4 key categorical features (e.g., education, marital-status, occupation) and plot their relationship with the target income variable using stacked bar charts. Summarize which categories show the strongest visual splits for predicting income level.
Contributor guide
Research direction
No file or test is named; start by locating the repository's Jupyter notebook and the code that loads the income dataset. Add stacked bar charts for 3–4 categorical features against income, then summarize which categories show the clearest visual separation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook
- Domain
- data-visualization
- Issue type
- Feature
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 55/100