iiitl / iiitl/Linear-Regression
Feature-target relationship ranking
Open
Nobody has claimed this yet.
exploration
medium
- Dominant language
- Jupyter Notebook
- Stars
- 0
- Forks
- 36
- PR merge metrics
- No merged PRs in 30d
Description
Create one correlation heatmap and rank features by absolute correlation with the target.
Visualize the top 5 feature-target relationships using scatter plots.
Summarize which features are most useful and why.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No file, notebook cell, dataset, or test is named in the issue. Start by locating the repository's analysis notebook and identifying the target and feature columns; the work is done when it contains a correlation heatmap, scatter plots for the five strongest feature-target relationships, and a written summary explaining their usefulness.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook
- Domain
- data-visualization, machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 55/100