---
metadata:
  - name: generator
    content: Diplodoc Platform v5.52.0
alternate:
  - https://catboost.ai/docs/en/concepts/python-reference_datasets_amazon.md
  - href: en/concepts/python-reference_datasets_amazon.md
    type: text/markdown
    title: Markdown version
  - href: ../llms.txt
    type: text/markdown
    title: llms.txt
---
> **Documentation Index:** Fetch the complete configuration index at https://catboost.ai/docs/en/llms.txt

# amazon

<!-- source: en/_includes/work_src/reusage-python/datasets__amazon__purpose-desc.md -->
Load the dataset from [Kaggle Amazon Employee Access Challenge](https://www.kaggle.com/c/amazon-employee-access-challenge/data).
<!-- endsource: en/_includes/work_src/reusage-python/datasets__amazon__purpose-desc.md -->


This dataset is best suited for binary classification.

The training dataset contains 32769 objects. Each object is described by 10 columns of numerical features. The `ACTION` column is used as the label.

The validation dataset contains 58921 objects. The structure is identical to the training dataset with the following variations:
- The `ACTION` column is omitted.
- The `id` column is added.

## Method call format {#call-format}

```python
amazon()
```

## Type of return value {#parameters}

<!-- source: en/_includes/work_src/reusage-python/datasets__output.md -->
A two pandas.DataFrame tuple (for train and validation datasets).
<!-- endsource: en/_includes/work_src/reusage-python/datasets__output.md -->


The train dataset contains the <q>ACTION</q> label.

## Usage examples {#usage-examples}

```python
from catboost.datasets import amazon
amazon_train, amazon_test = amazon()

print(amazon_train.head(3))
```

<!-- source: en/_includes/work_src/reusage-common-phrases/example-output.md -->
The output of this example:
<!-- endsource: en/_includes/work_src/reusage-common-phrases/example-output.md -->


```bash
   ACTION  RESOURCE  MGR_ID  ROLE_ROLLUP_1  ROLE_ROLLUP_2  ROLE_DEPTNAME  ROLE_TITLE  ROLE_FAMILY_DESC  ROLE_FAMILY  ROLE_CODE
0       1     39353   85475         117961         118300         123472      117905            117906       290919     117908
1       1     17183    1540         117961         118343         123125      118536            118536       308574     118539
2       1     36724   14457         118219         118220         117884      117879            267952        19721     11788
```