AutoKaggle: A Multi-Agent Framework for Autonomous Data Science
  Competitions
Data science tasks involving tabular data present complex challenges that
require sophisticated problem-solving approaches. We propose AutoKaggle, a
powerful and user-centric framework that assists data scientists in completing
daily data pipelines through a collaborative multi-agent system. AutoKaggle
implements an iterative development process that combines code execution,
debugging, and comprehensive unit testing to ensure code correctness and logic
consistency. The framework offers highly customizable workflows, allowing users
to intervene at each phase, thus integrating automated intelligence with human
expertise. Our universal data science toolkit, comprising validated functions
for data cleaning, feature engineering, and modeling, forms the foundation of
this solution, enhancing productivity by streamlining common tasks. We selected
8 Kaggle competitions to simulate data processing workflows in real-world
application scenarios. Evaluation results demonstrate that AutoKaggle achieves
a validation submission rate of 0.85 and a comprehensive score of 0.82 in
typical data science pipelines, fully proving its effectiveness and
practicality in handling complex data science tasks.
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Ziming Li
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Qianbo Zang
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David Ma
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madehua
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Jiawei Guo
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Tuney Zheng
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TY.Zheng
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Minghao Liu
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minghao
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Liam-Liu
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Xinyao Niu
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Xinyao Niu
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Yue Wang
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Jian Yang
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Jiaheng Liu
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2025-01-05T23:03:25.291Z
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Jiaheng Liu
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CheeryLJH
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Wanjun Zhong
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Wangchunshu Zhou
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2025-06-18T12:20:17.456Z
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Zhou
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Wangchunshu
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Wenhao Huang
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Ge Zhang
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2024-10-30T09:52:09.081Z
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Ge Zhang
user
zhangysk
AutoKaggle: A Multi-Agent Framework for Autonomous Data Science
  Competitions
Data science tasks involving tabular data present complex challenges that
require sophisticated problem-solving approaches. We propose AutoKaggle, a
powerful and user-centric framework that assists data scientists in completing
daily data pipelines through a collaborative multi-agent system. AutoKaggle
implements an iterative development process that combines code execution,
debugging, and comprehensive unit testing to ensure code correctness and logic
consistency. The framework offers highly customizable workflows, allowing users
to intervene at each phase, thus integrating automated intelligence with human
expertise. Our universal data science toolkit, comprising validated functions
for data cleaning, feature engineering, and modeling, forms the foundation of
this solution, enhancing productivity by streamlining common tasks. We selected
8 Kaggle competitions to simulate data processing workflows in real-world
application scenarios. Evaluation results demonstrate that AutoKaggle achieves
a validation submission rate of 0.85 and a comprehensive score of 0.82 in
typical data science pipelines, fully proving its effectiveness and
practicality in handling complex data science tasks.
AutoKaggle, a collaborative multi-agent system, enhances data science productivity through an iterative development process that combines code execution, debugging, and testing with human expertise and automated tools.
multi-agent system iterative development process code execution debugging unit testing data cleaning feature engineering modeling