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Add a leadership annual-giving upgrade notebook - #244

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shivamlalakiya merged 1 commit into
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e5-upgrade-notebook
Sep 25, 2026
Merged

shivamlalakiya merged 1 commit into
mainfrom
e5-upgrade-notebook

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@shivamlalakiya shivamlalakiya commented Sep 25, 2026 •

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Summary

  • Adds examples/notebooks/05_leadership_upgrade.ipynb: builds an upgrade training panel with build_upgrade_snapshots on top of make_donor_panel, plus a small synthetic activity log, fits MajorGiftClassifier, and validates it with FiscalYearGroupedSplitter's walk-forward split so no future fiscal year leaks into training.
  • Compares the model's top 20% by affinity score against a naive gave-500-plus-last-FY rule on the same out-of-fold predictions: the model's top 20% upgrades at roughly 21% versus about 15-16% for either version of the rule and 12% overall. The notebook states plainly, in its own markdown cell, that this is a smoke test on synthetic data and not a benchmark claim.
  • Lists the notebook in examples/README.md's notebook table and adds a CHANGELOG.md entry under Unreleased.

Test plan

  • python -m pytest --nbmake examples/notebooks -q --no-cov (all five notebooks, 01-05, re-executed): 5 passed
  • make ci: 2177 passed, 8 skipped, coverage 98.62% percent (floor 92 percent)
  • make riskcov: risk-tier coverage 98 percent (floor 93 percent)

Note: this branch was originally meant to stack on e4-upgrade-snapshots (#242), but that PR merged into main while this one was in progress, so it is rebased onto current main and opened against main directly instead.

Adds examples/notebooks/05_leadership_upgrade.ipynb: builds an upgrade
training panel with build_upgrade_snapshots on top of make_donor_panel
plus a small synthetic activity log, fits MajorGiftClassifier, and
validates it with FiscalYearGroupedSplitter's walk-forward split so no
future fiscal year leaks into training.

Compares the model's top 20% by affinity score against a naive "gave
$500+ last FY" rule on the same out-of-fold predictions: the model's
top 20% upgrades at roughly 21% versus about 15-16% for either version
of the rule and 12% overall. The notebook states plainly that this is
a smoke test on synthetic data, not a benchmark result.

Lists the notebook in examples/README.md and adds a CHANGELOG entry
under Unreleased.
@shivamlalakiya
shivamlalakiya merged commit eb6caa4 into main Sep 25, 2026
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