ds@mcpanalytics.aiEmail your data and your question and get an answer about your own data, with the method shown.Email your data and question. Get an answer.
Pipeline Case Studies: built to order | MCP Analytics
The pipeline's portfolio

Pipeline Case Studies

Commissioned by our own team on public datasets, built exactly like customer work. Every study below started as a plain-English question; the pipeline scoped the method, wrote the R, checked the result, and delivered the report.

Prebuilt analyses live in the library.

1
The question
“Which employee attributes actually predict attrition?”, asked in plain English against the public IBM HR dataset.
2
The build
The pipeline chose logistic regression for interpretable odds ratios plus XGBoost with SHAP for non-linear importance, then an independent pass checked the numbers and the narrative.
3
The delivered report
Retention levers ranked by role and department, per-employee risk, diagnostics shown. Interactive, citable, yours to keep.
Open the full report →
report sectionreport section

How the pipeline works →

IBM HR Employee Attrition Drivers, delivered report

People & HR

IBM HR Employee Attrition Drivers

Identifies which employee attributes predict attrition using logistic regression (interpretable odds ratios with 95% CI) and XGBoost with SHAP values…

Filter these by industry or question →The same studies, with the library's filters. The same question, at two depths →Three of these run as both a Snapshot and a full study, open them side by side. Choose your depth →A fast read, a one-page answer, or the full commissioned study.
WHERE NEXT
the buildInside the build pipeline →The stages that wrote, checked, and delivered these studies.price itPricing →What each tier costs and what it delivers.talk to usBook a demo →Walk through the platform with us before you commit.