Add dataset card

README.md

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+---
+license: mit
+task_categories:
+  - tabular-classification
+  - tabular-regression
+language:
+  - en
+size_categories:
+  - n<1K
+  - 1K<n<10K
+tags:
+  - imputation
+  - missing-data
+  - tabular
+pretty_name: imputify datasets
+configs:
+  - config_name: iris
+    data_files: iris.parquet
+  - config_name: wine
+    data_files: wine.parquet
+  - config_name: diabetes
+    data_files: diabetes.parquet
+  - config_name: breast_cancer
+    data_files: breast_cancer.parquet
+  - config_name: titanic
+    data_files: titanic.parquet
+  - config_name: heart_disease
+    data_files: heart_disease.parquet
+  - config_name: blood_transfusion
+    data_files: blood_transfusion.parquet
+  - config_name: thalassemia
+    data_files: thalassemia.parquet
+  - config_name: ilpd
+    data_files: ilpd.parquet
+  - config_name: spas_agri
+    data_files: spas_agri.parquet
+---
+
+# imputify datasets
+
+Curated tabular datasets bundled with the [`imputify`](https://github.com/gabfssilva/imputify)
+library for examples, tests, and missing-data benchmarks. Every file is a single parquet:
+feature columns first, target column last, no missing values. Column names are
+`snake_case`; columns that the upstream sources include for identification but not
+modeling (IDs, free-text names) have been dropped.
+
+## Usage
+
+```python
+from imputify import load, introduce_missing
+
+X, y = load("iris")                              # clean
+X_missing, mask = introduce_missing(X, 0.3)      # ampute for experiments
+```
+
+`load` caches downloads through `huggingface_hub` (default `~/.cache/huggingface/hub`).
+The complete catalogue is exposed as `imputify.DATASETS` and the literal type as
+`imputify.Dataset`.
+
+## Catalogue
+
+| Name | Rows | Features | Numeric | Categorical | Target dtype | Original source |
+|------|-----:|---------:|--------:|------------:|--------------|-----------------|
+| `iris`              |   150 |  4 |  4 | 0 | `int64`    | `sklearn.datasets.load_iris` (Fisher, 1936) |
+| `wine`              |   178 | 13 | 13 | 0 | `int64`    | `sklearn.datasets.load_wine` (UCI Wine Recognition) |
+| `diabetes`          |   442 | 10 | 10 | 0 | `float64`  | `sklearn.datasets.load_diabetes` (Efron et al., 2004) |
+| `breast_cancer`     |   569 | 30 | 30 | 0 | `int64`    | `sklearn.datasets.load_breast_cancer` (UCI WDBC) |
+| `titanic`           | 1 043 |  7 |  5 | 2 | `category` | OpenML `titanic` |
+| `heart_disease`     |   270 | 13 | 13 | 0 | `category` | UCI Heart Disease / OpenML `heart-statlog` |
+| `blood_transfusion` |   748 |  4 |  4 | 0 | `category` | UCI Blood Transfusion Service Center |
+| `thalassemia`       |   606 | 18 | 12 | 6 | `object`   | Kaggle-derived clinical dataset |
+| `ilpd`              |   583 | 10 |  9 | 1 | `category` | UCI Indian Liver Patient Dataset (ILPD) |
+| `spas_agri`         | 4 191 | 14 |  9 | 5 | `object`   | Kaggle-derived agricultural dataset |
+
+Counts come from running the loader once: rows = `len(X)`, features = `X.shape[1]`,
+numeric/categorical split = `select_dtypes(...)`.
+
+## Format
+
+Each parquet has the **target** as the last column. `imputify.load(name)` splits it back
+into `(X, y)`:
+
+```python
+df = pd.read_parquet("iris.parquet")
+X, y = df.drop(columns=["target"]), df["target"]
+```
+
+Categorical features are stored as pandas `category` (titanic) or `object`
+(thalassemia, spas_agri) and round-trip through parquet without manual casting.
+
+## Licensing & attribution
+
+Datasets carry their original licences. `iris`, `wine`, `diabetes`, and `breast_cancer`
+ship with scikit-learn (BSD-compatible). The UCI / OpenML datasets are redistributed
+under their respective terms — cite the original sources in publications. The
+`thalassemia` and `spas_agri` derivatives keep the upstream Kaggle terms.