ACM-VIT / ACM-VIT/Good-Client-Bad-Client
Step 5: EDA and Vintage Analysis
- Dominant language
- Jupyter Notebook
- Stars
- 20
- Forks
- 7
- PR merge metrics
- No merged PRs in 30d
Description
**EDA and Vintage Analysis**
Perform EDA for the data set to find best factors to be considered for the model.
What is Vintage Analysis could be searched [here](https://www.listendata.com/2019/09/credit-risk-vintage-analysis.html).
**Where to show**
Make all the Analysis under the Observation Heading.
Contributor guide
No contributing guide indexed for this repository
Research direction
Start by locating the Observation heading in the repository's Jupyter Notebook and reviewing the referenced data set and model context. Perform exploratory data analysis and Vintage Analysis to identify factors for consideration, then place all analysis under Observation. The issue names no specific notebook, dataset fields, or tests, so the expected scope needs clarification.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, machine-learning
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100