---
metadata:
  - name: generator
    content: Diplodoc Platform v5.52.0
alternate:
  - https://catboost.ai/docs/en/concepts/python-reference_datasets.md
  - href: en/concepts/python-reference_datasets.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

# datasets

Datasets processing.

## Methods {#methods}
### [adult](https://catboost.ai/docs/en/concepts/python-reference_datasets_uci-adult.md)

<!-- source: en/_includes/work_src/reusage-python/datasets__adult__uci.md -->
Load the [UCI Adult Data Set](https://archive.ics.uci.edu/ml/datasets/Adult).
<!-- endsource: en/_includes/work_src/reusage-python/datasets__adult__uci.md -->

### [amazon](https://catboost.ai/docs/en/concepts/python-reference_datasets_amazon.md)

<!-- 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 -->

### [epsilon](https://catboost.ai/docs/en/concepts/python-reference_datasets_epsilon.md)

<!-- source: en/_includes/work_src/reusage-python/datasets__epsilon.md -->
Load the [Epsilon dataset](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#epsilon).
<!-- endsource: en/_includes/work_src/reusage-python/datasets__epsilon.md -->

### [higgs](https://catboost.ai/docs/en/concepts/python-reference_datasets_higgs.md)

<!-- source: en/_includes/work_src/reusage-python/datasets__higgs.md -->
Load the [HIGGS Data Set](https://archive.ics.uci.edu/ml/datasets/HIGGS).
<!-- endsource: en/_includes/work_src/reusage-python/datasets__higgs.md -->

### [monotonic1](https://catboost.ai/docs/en/concepts/python-reference_datasets_monotonic1.md)

<!-- source: en/_includes/work_src/reusage-python/datasets__monotonic1.md -->
Load the Yandex dataset with monotonic constraints. This dataset contains categorical features.
<!-- endsource: en/_includes/work_src/reusage-python/datasets__monotonic1.md -->

### [monotonic2](https://catboost.ai/docs/en/concepts/python-reference_datasets_monotonic2.md)

<!-- source: en/_includes/work_src/reusage-python/datasets__monotonic2.md -->
Load the Yandex dataset with monotonic constraints. This dataset does not contain categorical features.
<!-- endsource: en/_includes/work_src/reusage-python/datasets__monotonic2.md -->

### [msrank](https://catboost.ai/docs/en/concepts/python-reference_datasets_msrank.md)

<!-- source: en/_includes/work_src/reusage-python/datasets__msrank.md -->
Load the [Microsoft Learning to Rank Dataset](https://www.microsoft.com/en-us/research/project/mslr/).
<!-- endsource: en/_includes/work_src/reusage-python/datasets__msrank.md -->

### [msrank_10k](https://catboost.ai/docs/en/concepts/python-reference_datasets_msrank_10k.md)

<!-- source: en/_includes/work_src/reusage-python/datasets__msrank_10k.md -->
Load a smaller version of the [Microsoft Learning to Rank Dataset](https://www.microsoft.com/en-us/research/project/mslr/). This dataset is a shrunk version of the [msrank](https://catboost.ai/docs/en/concepts/python-reference_datasets_msrank.md) dataset.
<!-- endsource: en/_includes/work_src/reusage-python/datasets__msrank_10k.md -->

### [rotten_tomatoes](https://catboost.ai/docs/en/concepts/python-reference_datasets_rotten_tomatoes.md)

<!-- source: en/_includes/work_src/reusage-python/dataset__rotten_tomatoes.md -->
Load the preprocessed [Rotten Tomatoes dataset](https://www.kaggle.com/rpnuser8182/rotten-tomatoes). This version can be used as a simple matrix-like pool.
<!-- endsource: en/_includes/work_src/reusage-python/dataset__rotten_tomatoes.md -->

### [titanic](https://catboost.ai/docs/en/concepts/python-reference_datasets_titanic.md)

<!-- source: en/_includes/work_src/reusage-python/datasets__titanic__purpose-desc.md -->
Load the dataset from [Kaggle Titanic: Machine Learning from Disaster](https://www.kaggle.com/c/titanic/data).
<!-- endsource: en/_includes/work_src/reusage-python/datasets__titanic__purpose-desc.md -->