Select datasets, fields, and records for CRISP-DM modeling datasets.
npx skills add https://github.com/thbraet/claude-template --skill select-data
Please help me install this Agent Skill. Skill: select-data Source: https://github.com/thbraet/claude-template/tree/main/skills/select-data Command: npx skills add https://github.com/thbraet/claude-template --skill select-data
Select-data helps data teams decide which datasets, fields, and records to carry forward into modeling, ensuring traceability of inclusion decisions and alignment to CRISP-DM goals.
Run the data selection workflow against your collected data and review the generated notebook and summary to confirm the chosen data subset.
High-intent search queries and answers about installing and using this skill.
Selecting the right data for modeling involves deciding which datasets, fields, and records to carry forward based on CRISP-DM Phase 2 data understanding. This ensures traceability of inclusion decisions and alignment with defined data mining goals.
Data selection requires Phase 2 documentation because it contains the 1.3 data mining goals and 2.x data understanding outputs. These sources provide the necessary context to decide which datasets, fields, and records meet quality considerations for modeling.
Document dataset inclusion criteria by generating a Jupyter notebook at notebooks/3.1-select-data.ipynb containing analysis and rationale, plus a summary document at docs/crisp-dm/3-data-preparation/3.1-select-data.md. This enables reproducible data selection workflows.
Yes, this data selection workflow applies to CRISP-DM 3.x data preparation across projects with multiple data sources and quality considerations. It determines exactly which datasets, fields, and records from Phase 2 will be carried forward into the modeling dataset.
The best way to decide which fields and records to carry forward is to apply CRISP-DM data selection criteria against your collected data. This articulates inclusion rationale and produces a summary document confirming the chosen data subset for modeling.
thbraetTranslate business objectives into data mining goals with success criteria.
thbraetApply type casting, column ordering, and deterministic splits to finalize datasets for modeling.
thbraetDerive and transform features from cleaned datasets for CRISP-DM workflows.
thbraetAssesses datasets for completeness, correctness, consistency, and timeliness.
