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+# NannyML Datasets
+
+This Python package contains our curated datasets, used for testing and product demos.
+
+We provide datasets for the following problem types: binary classification, multiclass classification and regression.
+
+
+## Anatomy of a `Dataset`
+
+Each `Dataset` has `reference` and `monitoring` properties. Each of these exposes the following properties:
+
+- `data`: access the full dataset as a `pyarrow.Table`
+- `predictions`: access the model predictions as a `numpy.ndarray`
+- `predicted_probabilities`: access the model's predicted probablilities. Only available for classification datasets. For binary classification datasets this will be a single 
+- `targets`: access the model targets as a `numpy.ndarray`
+- `timestamps`: access the model timestamps as a `numpy.ndarray`
+- `categorical_features`: access the model's categorical features as an `Iterable` of `numpy.ndarray`
+- `continuous_features`: access the model's continuous features as an `Iterable` of `numpy.ndarray`
+- `features`: access the model's features as an `Iterable` of `numpy.ndarray`
+
+If any of these properties are not available, trying to access them will raise an `AssertionError`.
+
+
+## Example usage
+
+```python
+
+from nannyml_dataset.binary_classification import synthetic_car_loan  # Import the dataset
+
+print(synthetic_car_loan.reference.timestamps)  # Access some reference property
+print(synthetic_car_loan.monitoring.timestamps)  # Access some monitoring property
+
+for col in synthetic_car_loan.reference.categorical_features:  # Loop over reference categorical features
+    print(col)  # You can do more useful stuff here, like setting up a univariate covariate shift monitor!
+
+```
+
+## Available datasets
+
+### Binary Classification
+
+
+| Dataset | Synthetic | Description |
+|---------|-----------|-------------|
+| synthetic_car_loan | yes | A synthetic dataset describing a model that predicts defaulting a loan for a car. |
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