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LICENSE.txt

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+Creative Commons Attribution 4.0 International (CC BY 4.0)
+
+You are free to:
+• Share — copy and redistribute the material in any medium or format.
+• Adapt — remix, transform, and build upon the material for any purpose, even commercially.
+
+Under the following terms:
+• Attribution — You must give appropriate credit, provide a link to the license,
+  and indicate if changes were made, without suggesting endorsement.
+
+Full license text: https://creativecommons.org/licenses/by/4.0/
+
+© 2025 Tarek Masryo
+This dataset is released under the CC BY 4.0 International license.

README_HF.md

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+---
+license: cc-by-4.0
+language:
+- en
+task_categories:
+- tabular-classification
+- tabular-regression
+tags:
+- blood-donation
+- transfusion
+- healthcare-operations
+- synthetic
+- tabular-data
+- decision-support
+- calibration
+- thresholding
+- risk-scoring
+size_categories:
+- 10K<n<100K
+pretty_name: Blood Donation Registry — Synthetic Donors, Prevalence & Compatibility
+---
+
+# 🩸 Blood Donation Registry — Synthetic Donors, Prevalence & Compatibility
+
+A synthetic blood donation operations dataset for **analysis and decision-focused modeling**: eligibility/deferrals, donation history, rare blood types, country-level prevalence, and RBC transfusion compatibility (ABO/Rh).
+
+> ✅ **Synthetic data** (safe for experimentation/teaching).  
+> ⚠️ **Not clinical/medical ground truth** and not intended for real-world medical decision-making.
+
+---
+
+## Dataset summary
+
+This dataset is designed to support portfolio-grade notebooks and practical workflows:
+- exploratory analysis (EDA) and segmentation
+- propensity / likelihood modeling and **calibration**
+- threshold selection under capacity/budget constraints
+- rare-blood availability analysis using prevalence + compatibility rules
+
+---
+
+## Data files
+
+### 1) `blood_donation_registry_ml_ready.csv` (30,000 rows × 27 columns)
+Donor-level snapshot with a fixed reference date:
+- `as_of_date = 2024-12-31`
+
+**Key groups**
+- **Identity & geography:** `donor_id` (unique), `country_code`, `region`
+- **Profile:** `age`, `sex` (M/F), `bmi`, `smoker` (0/1), `chronic_condition_flag` (0/1)
+- **Eligibility & deferrals:** `eligibility_status`, `eligible_to_donate` (0/1), `deferral_reason`
+- **Donation behavior/history:** `donation_count_last_12m`, `lifetime_donation_count`, `first_donation_year`,
+  `years_since_first_donation`, `last_donation_date`, `recency_days`, `is_regular_donor` (0/1),
+  `donor_age_at_first_donation`, `preferred_site`
+- **Blood context:** `blood_type` (8 types), `is_rare_type` (0/1), `blood_type_country_prevalence`
+- **Engineered score (optional):** `donation_propensity_score`
+
+**Outcome columns**
+- `donated_next_6m` (0/1)
+- `next_6m_donation_count` (0–3)
+
+---
+
+### 2) `blood_population_distribution.csv` (39 rows × 12 columns)
+Country-level population + blood type prevalence:
+- `country_code`, `region`, `population_size`
+- proportions: `p_o_pos`, `p_o_neg`, `p_a_pos`, `p_a_neg`, `p_b_pos`, `p_b_neg`, `p_ab_pos`, `p_ab_neg`
+- `rh_negative_rate`
+
+**Integrity note:** blood type proportions sum to **1.0** per country.
+
+---
+
+### 3) `blood_compatibility_lookup.csv` (64 rows × 4 columns)
+RBC transfusion compatibility matrix:
+- `donor_blood_type`, `recipient_blood_type`
+- `compatible_for_rbc_transfusion` (0/1)
+- `compatibility_level`: `ideal | acceptable | incompatible`
+
+---
+
+### 4) `data_dictionary.csv`
+Column-level documentation:
+- `file`, `column_name`, `type`, `description`, `allowed_values_or_range`, `missing_values`
+
+---
+
+## Relationships (how tables connect)
+
+- `blood_donation_registry_ml_ready.csv.country_code`
+  ↔ `blood_population_distribution.csv.country_code`
+
+- `blood_donation_registry_ml_ready.csv.blood_type_country_prevalence`
+  is derived from the matching country prevalence table.
+
+- `blood_compatibility_lookup.csv` provides rule-based compatibility for blood type pairs.
+
+---
+
+## Recommended tasks
+
+### 1) Donation likelihood (binary classification)
+- Outcome: `donated_next_6m`
+- Suggested evaluation: `ROC-AUC`, `PR-AUC`, `F1`, plus **calibration** (reliability curve / Brier score)
+
+### 2) Donation frequency (count prediction)
+- Outcome: `next_6m_donation_count`
+- Suggested evaluation: `MAE`, `RMSE` (optional Poisson/Ordinal baselines)
+
+### 3) Decision policy under constraints
+Turn probabilities into an outreach policy:
+- choose an operating threshold given **capacity/budget**
+- compare FP/FN tradeoffs
+- validate stability across segments (country/region, rare types, eligibility)
+
+### 4) Rare blood operations analytics
+- analyze `is_rare_type` by country prevalence
+- explore compatibility-aware matching using the lookup matrix
+
+---
+
+## Modeling notes (avoid leakage / shortcuts)
+
+This dataset intentionally includes engineered and overlapping fields for different notebook styles.
+
+- `donated_next_6m` is derived from `next_6m_donation_count` → use **one** outcome as the target.
+- `eligible_to_donate` overlaps with `eligibility_status` → keep one for simpler baselines.
+- `eligible_to_donate == 0` implies `donated_next_6m == 0` → for behavior modeling, consider restricting training to `eligible_to_donate == 1`.
+- `donation_propensity_score` is an engineered signal; exclude it for “feature-only” benchmark baselines.
+
+---
+
+## Data quality expectations
+
+- `donor_id` is unique (no duplicate donors)
+- no duplicate rows
+- snapshot consistency (`as_of_date` fixed)
+- `recency_days` aligns with `as_of_date - last_donation_date`
+- country codes match the prevalence table
+- compatibility lookup covers all 8×8 donor/recipient pairs
+
+---
+
+## Synthetic data generation (high-level)
+
+Records are simulated to reflect realistic constraints and patterns:
+- eligibility rules and deferral reasons (age/BMI/health flags)
+- donation history distributions and recency behavior
+- country-level blood type prevalence used to derive per-donor prevalence context
+- ABO/Rh compatibility rules encoded in the lookup table
+
+Synthetic distributions may not match any specific real-world population.
+
+---
+
+## Limitations
+
+- Snapshot-style dataset (not a full longitudinal event log beyond history fields)
+- Synthetic distributions may differ from real operational settings
+- Engineered signals (e.g., `donation_propensity_score`) can act as shortcut features if used without care
+
+---
+
+## Quick start
+
+```python
+import pandas as pd
+
+donors = pd.read_csv("blood_donation_registry_ml_ready.csv")
+pop    = pd.read_csv("blood_population_distribution.csv")
+compat = pd.read_csv("blood_compatibility_lookup.csv")
+
+# Example join: add country population
+donors = donors.merge(pop[["country_code", "population_size"]], on="country_code", how="left")
+print(donors.shape)
+```
+
+---
+
+## License
+
+CC BY 4.0 — attribution required.
+
+---
+
+## Author
+
+Tarek Masryo

data/artifacts/blood_type_compatible_supply_and_stress.csv

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+blood_type,compatible_supply_share,demand_share,stress_index
+O-,0.0259,0.025515793694791095,0.9851657797216639
+O+,0.44753333333333334,0.41934100132021573,0.9370050677496254
+A-,0.05246666666666666,0.027190592223026385,0.5182450868429426
+A+,0.7313666666666666,0.25904530158987005,0.3541934755797868
+B-,0.04493333333333333,0.01901686822466964,0.4232240702819653
+B+,0.6398333333333334,0.17190004670611025,0.2686637875062937
+AB-,0.08333333333333333,0.011972155777675908,0.1436658693321109
+AB+,1.0,0.06601824046364094,0.06601824046364094

data/artifacts/blood_type_donor_share.csv

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+blood_type,donor_share
+A+,0.25726666666666664
+A-,0.026566666666666666
+AB+,0.0645
+AB-,0.011833333333333333
+B+,0.17326666666666668
+B-,0.019033333333333333
+O+,0.42163333333333336
+O-,0.0259

data/artifacts/blood_type_population_share.csv

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+blood_type,population_share
+A+,0.25904530158987005
+A-,0.027190592223026385
+AB+,0.06601824046364094
+AB-,0.011972155777675908
+B+,0.17190004670611025
+B-,0.01901686822466964
+O+,0.41934100132021573
+O-,0.025515793694791095

data/artifacts/blood_type_representation_gap.csv

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+blood_type,gap
+A+,-0.0017786349232034038
+A-,-0.0006239255563597196
+AB+,-0.0015182404636409352
+AB-,-0.00013882244434257514
+B+,0.0013666199605564255
+B-,1.6465108663692857e-05
+O+,0.002292332013117626
+O-,0.0003842063052089048

data/blood_compatibility_lookup.csv

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+donor_blood_type,recipient_blood_type,compatible_for_rbc_transfusion,compatibility_level
+O+,O+,1,ideal
+O+,O-,0,incompatible
+O+,A+,1,acceptable
+O+,A-,0,incompatible
+O+,B+,1,acceptable
+O+,B-,0,incompatible
+O+,AB+,1,acceptable
+O+,AB-,0,incompatible
+O-,O+,1,acceptable
+O-,O-,1,ideal
+O-,A+,1,acceptable
+O-,A-,1,acceptable
+O-,B+,1,acceptable
+O-,B-,1,acceptable
+O-,AB+,1,acceptable
+O-,AB-,1,acceptable
+A+,O+,0,incompatible
+A+,O-,0,incompatible
+A+,A+,1,ideal
+A+,A-,0,incompatible
+A+,B+,0,incompatible
+A+,B-,0,incompatible
+A+,AB+,1,acceptable
+A+,AB-,0,incompatible
+A-,O+,0,incompatible
+A-,O-,0,incompatible
+A-,A+,1,acceptable
+A-,A-,1,ideal
+A-,B+,0,incompatible
+A-,B-,0,incompatible
+A-,AB+,1,acceptable
+A-,AB-,1,acceptable
+B+,O+,0,incompatible
+B+,O-,0,incompatible
+B+,A+,0,incompatible
+B+,A-,0,incompatible
+B+,B+,1,ideal
+B+,B-,0,incompatible
+B+,AB+,1,acceptable
+B+,AB-,0,incompatible
+B-,O+,0,incompatible
+B-,O-,0,incompatible
+B-,A+,0,incompatible
+B-,A-,0,incompatible
+B-,B+,1,acceptable
+B-,B-,1,ideal
+B-,AB+,1,acceptable
+B-,AB-,1,acceptable
+AB+,O+,0,incompatible
+AB+,O-,0,incompatible
+AB+,A+,0,incompatible
+AB+,A-,0,incompatible
+AB+,B+,0,incompatible
+AB+,B-,0,incompatible
+AB+,AB+,1,ideal
+AB+,AB-,0,incompatible
+AB-,O+,0,incompatible
+AB-,O-,0,incompatible
+AB-,A+,0,incompatible
+AB-,A-,0,incompatible
+AB-,B+,0,incompatible
+AB-,B-,0,incompatible
+AB-,AB+,1,acceptable
+AB-,AB-,1,ideal

data/blood_population_distribution.csv

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+country_code,region,population_size,p_o_pos,p_o_neg,p_a_pos,p_a_neg,p_b_pos,p_b_neg,p_ab_pos,p_ab_neg,rh_negative_rate
+GB,Europe,108328085,0.373554,0.056414,0.305723,0.06497,0.109383,0.023904,0.04371,0.022342,0.16763
+FR,Europe,46738553,0.330104,0.071148,0.336667,0.072512,0.095727,0.023263,0.049985,0.020594,0.187517
+DE,Europe,10585377,0.368965,0.058713,0.333185,0.060172,0.105269,0.013257,0.03894,0.021499,0.153641
+IT,Europe,69157848,0.361326,0.053443,0.315731,0.09813,0.086372,0.029235,0.033287,0.022476,0.203284
+ES,Europe,151099456,0.383377,0.069177,0.312493,0.061924,0.070285,0.026869,0.061681,0.014194,0.172164
+PL,Europe,182033766,0.381368,0.05893,0.302474,0.06035,0.088047,0.014043,0.081043,0.013745,0.147068
+SE,Europe,25637222,0.399913,0.052415,0.291278,0.06188,0.099387,0.020873,0.04895,0.025304,0.160472
+NO,Europe,12274621,0.374214,0.066608,0.295801,0.05991,0.121795,0.018997,0.04615,0.016525,0.16204
+EG,MENA,51905866,0.400264,0.010721,0.272707,0.026238,0.189222,0.017832,0.07368,0.009336,0.064127
+SA,MENA,157331731,0.388597,0.016553,0.287097,0.033202,0.19248,0.013702,0.061215,0.007154,0.070611
+AE,MENA,111169700,0.393242,0.023836,0.267321,0.028623,0.207414,0.013437,0.055337,0.01079,0.076686
+MA,MENA,159922984,0.395024,0.02047,0.271746,0.030138,0.191904,0.027021,0.05834,0.005357,0.082986
+DZ,MENA,88343264,0.386948,0.020447,0.264067,0.041195,0.165802,0.025768,0.085957,0.009816,0.097226
+TN,MENA,192906558,0.393744,0.041856,0.246389,0.033342,0.175958,0.024235,0.07541,0.009066,0.108499
+JO,MENA,189225564,0.404445,0.017186,0.259499,0.020144,0.171779,0.03179,0.087001,0.008156,0.077276
+TR,MENA,132997589,0.408727,0.011384,0.260613,0.035734,0.193888,0.018138,0.063463,0.008053,0.073309
+CN,Asia,110011636,0.40232,0.003364,0.300665,0.008761,0.214937,0.011365,0.05546,0.003128,0.026618
+JP,Asia,127838555,0.386886,0.006329,0.26758,0.013454,0.235997,0.022858,0.056503,0.010393,0.053034
+KR,Asia,146713028,0.40836,0.013308,0.204147,0.00566,0.270412,0.004489,0.089149,0.004475,0.027932
+IN,Asia,104912284,0.401338,0.017053,0.266909,0.013407,0.200025,0.009857,0.080442,0.010969,0.051286
+PK,Asia,163566703,0.383166,0.010457,0.260069,0.008032,0.241748,0.01231,0.077502,0.006716,0.037515
+BD,Asia,92372540,0.415637,0.00785,0.257909,0.012049,0.236964,0.005101,0.057753,0.006737,0.031737
+ID,Asia,19310852,0.389487,0.008585,0.245145,0.02112,0.249304,0.012892,0.067633,0.005834,0.048431
+PH,Asia,25020885,0.397736,0.01857,0.28997,0.011624,0.199503,0.007758,0.06973,0.005109,0.043061
+VN,Asia,107967805,0.372256,0.008501,0.291794,0.008721,0.230565,0.008199,0.073724,0.00624,0.031661
+TH,Asia,127881648,0.372552,0.00222,0.300781,0.008114,0.222665,0.008853,0.079396,0.005419,0.024606
+NG,Africa,163563548,0.503097,0.020604,0.204142,0.014725,0.18264,0.012502,0.05091,0.01138,0.059211
+ZA,Africa,160148712,0.500193,0.012546,0.177415,0.010274,0.187636,0.019432,0.084624,0.00788,0.050132
+KE,Africa,198119899,0.477615,0.007394,0.211384,0.010109,0.181184,0.024877,0.074998,0.012439,0.054819
+ET,Africa,124415538,0.429899,0.008897,0.203755,0.02723,0.218581,0.024657,0.075716,0.011265,0.072049
+GH,Africa,59395058,0.507883,0.013233,0.241751,0.004708,0.154133,0.019648,0.04938,0.009264,0.046853
+UG,Africa,138600085,0.461575,0.022513,0.193424,0.012742,0.191738,0.02853,0.081782,0.007696,0.071481
+US,Americas,136285389,0.43081,0.039794,0.282108,0.031338,0.129351,0.016618,0.056606,0.013375,0.101125
+CA,Americas,104855910,0.482602,0.035839,0.272835,0.030648,0.103616,0.01348,0.049632,0.011348,0.091315
+MX,Americas,195510719,0.472418,0.033551,0.23214,0.027658,0.140294,0.024059,0.041887,0.027993,0.113261
+BR,Americas,100164033,0.432502,0.052956,0.286272,0.030208,0.113818,0.018506,0.039516,0.026222,0.127892
+AR,Americas,78362381,0.445333,0.038205,0.294449,0.027046,0.080378,0.038168,0.062605,0.013816,0.117235
+CL,Americas,158200541,0.461729,0.025966,0.250675,0.029253,0.123028,0.015724,0.064316,0.029309,0.100252
+CO,Americas,17876696,0.443579,0.055605,0.264147,0.021333,0.120323,0.024594,0.054632,0.015787,0.117319

docs/data_dictionary.csv

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+file,column_name,type,description,allowed_values_or_range,missing_values
+blood_donation_registry_ml_ready.csv,donor_id,int,Unique donor identifier (synthetic).,unique integer,none
+blood_donation_registry_ml_ready.csv,age,int,Donor age at as_of_date (years).,18-69 (observed),none
+blood_donation_registry_ml_ready.csv,sex,category,Self-reported sex (synthetic).,"{F, M}",none
+blood_donation_registry_ml_ready.csv,country_code,category,ISO 3166-1 alpha-2 country code.,ISO 3166-1 alpha-2; 39 unique values,none
+blood_donation_registry_ml_ready.csv,region,category,Geographic region grouping.,"{Africa, Americas, Asia, Europe, MENA}",none
+blood_donation_registry_ml_ready.csv,blood_type,category,ABO/Rh blood type.,"{A+, A-, AB+, AB-, B+, B-, O+, O-}",none
+blood_donation_registry_ml_ready.csv,is_rare_type,bool,Flag for rare blood type within the donor’s country (based on prevalence).,"{0,1}",none
+blood_donation_registry_ml_ready.csv,smoker,bool,Smoking status flag (synthetic).,"{0,1}",none
+blood_donation_registry_ml_ready.csv,bmi,float,Body Mass Index.,16-42 (observed),none
+blood_donation_registry_ml_ready.csv,chronic_condition_flag,bool,"Indicator for chronic condition (synthetic). 1 if deferral_reason == chronic_condition, else 0.","{0,1}",none
+blood_donation_registry_ml_ready.csv,eligible_to_donate,bool,Eligibility flag at as_of_date; redundant with eligibility_status.,"{0,1}",none
+blood_donation_registry_ml_ready.csv,deferral_reason,category,Reason for deferral when not eligible; NaN when eligibility_status==eligible.,"{age_out_of_range, bmi_out_of_range, chronic_condition}",NaN when eligible
+blood_donation_registry_ml_ready.csv,preferred_site,category,Preferred donation site type.,"{community_camp, hospital, mobile_unit}",none
+blood_donation_registry_ml_ready.csv,donation_count_last_12m,int,Number of donations in the last 12 months before as_of_date.,0-5 (observed),none
+blood_donation_registry_ml_ready.csv,is_regular_donor,bool,Flag indicating a regular donation pattern (synthetic).,"{0,1}",none
+blood_donation_registry_ml_ready.csv,years_since_first_donation,int,Years since first recorded donation year.,0-35 (observed),none
+blood_donation_registry_ml_ready.csv,lifetime_donation_count,int,Total lifetime donations (synthetic).,1-140 (observed),none
+blood_donation_registry_ml_ready.csv,first_donation_year,int,Year of first recorded donation.,1989-2024 (observed),none
+blood_donation_registry_ml_ready.csv,last_donation_date,date,Date of the most recent donation prior to as_of_date.,YYYY-MM-DD (range: 2007-01-23-2024-12-30),none
+blood_donation_registry_ml_ready.csv,recency_days,int,Days since last donation: as_of_date - last_donation_date.,1-6552 (observed),none
+blood_donation_registry_ml_ready.csv,donor_age_at_first_donation,int,Age at first donation: age - years_since_first_donation.,18-68 (observed),none
+blood_donation_registry_ml_ready.csv,blood_type_country_prevalence,float,Prevalence of the donor’s blood type in their country (from blood_population_distribution).,0-1 (observed: 0.00222-0.50788),none
+blood_donation_registry_ml_ready.csv,donation_propensity_score,float,Synthetic propensity score feature (not a real-world model output).,0-100 (observed: 0-89.2),none
+blood_donation_registry_ml_ready.csv,eligibility_status,category,"Eligibility class at as_of_date: eligible, temporary_deferral, permanent_deferral.","{eligible, permanent_deferral, temporary_deferral}",none
+blood_donation_registry_ml_ready.csv,as_of_date,date,Reference snapshot date for the record.,YYYY-MM-DD (fixed: 2024-12-31),none
+blood_donation_registry_ml_ready.csv,donated_next_6m,bool,Binary label derived from next_6m_donation_count (>0).,"{0,1}",none
+blood_donation_registry_ml_ready.csv,next_6m_donation_count,int,Number of donation events in the next 6 months (synthetic). Capped at 3 for cadence realism.,0-3 (observed),none
+blood_compatibility_lookup.csv,donor_blood_type,category,Donor blood type for RBC transfusion compatibility lookup.,"{A+, A-, AB+, AB-, B+, B-, O+, O-}",none
+blood_compatibility_lookup.csv,recipient_blood_type,category,Recipient blood type for RBC transfusion compatibility lookup.,"{A+, A-, AB+, AB-, B+, B-, O+, O-}",none
+blood_compatibility_lookup.csv,compatible_for_rbc_transfusion,bool,Compatibility flag for RBC transfusion (donor → recipient).,"{0,1}",none
+blood_compatibility_lookup.csv,compatibility_level,category,Compatibility level label.,"{acceptable, ideal, incompatible}",none
+blood_population_distribution.csv,country_code,category,ISO 3166-1 alpha-2 country code.,ISO 3166-1 alpha-2; 39 unique values,none
+blood_population_distribution.csv,region,category,Region grouping used in the main table.,"{Africa, Americas, Asia, Europe, MENA}",none
+blood_population_distribution.csv,population_size,int,Synthetic country population size used for weighting/realism.,10585377-198119899 (observed),none
+blood_population_distribution.csv,p_o_pos,float,Prevalence of blood type O POS in the country.,0-1 (observed: 0.3301-0.50788),none
+blood_population_distribution.csv,p_o_neg,float,Prevalence of blood type O NEG in the country.,0-1 (observed: 0.00222-0.071148),none
+blood_population_distribution.csv,p_a_pos,float,Prevalence of blood type A POS in the country.,0-1 (observed: 0.17741-0.33667),none
+blood_population_distribution.csv,p_a_neg,float,Prevalence of blood type A NEG in the country.,0-1 (observed: 0.004708-0.09813),none
+blood_population_distribution.csv,p_b_pos,float,Prevalence of blood type B POS in the country.,0-1 (observed: 0.070285-0.27041),none
+blood_population_distribution.csv,p_b_neg,float,Prevalence of blood type B NEG in the country.,0-1 (observed: 0.004489-0.038168),none
+blood_population_distribution.csv,p_ab_pos,float,Prevalence of blood type AB POS in the country.,0-1 (observed: 0.033287-0.089149),none
+blood_population_distribution.csv,p_ab_neg,float,Prevalence of blood type AB NEG in the country.,0-1 (observed: 0.003128-0.029309),none
+blood_population_distribution.csv,rh_negative_rate,float,Country-level Rh- rate (sum of negative blood types).,0-1 (observed: 0.024606-0.20328),none