title: AutoKaggle: A Multi-Agent Framework for Autonomous Data Science
    Competitions
summary: 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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paper_authors.name: Ziming Li
paper_authors._id: 6721abd9c8acb97a181c3675
paper_authors.name: Qianbo Zang
paper_authors._id: 6721abd9c8acb97a181c3676
paper_authors.name: David Ma
paper_authors._id: 6721abd9c8acb97a181c3677
paper_authors.name: Jiawei Guo
paper_authors._id: 6721abd9c8acb97a181c3678
paper_authors.name: Tuney Zheng
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paper_authors.user: aaabiao
paper_authors._id: 6721abd9c8acb97a181c3679
paper_authors.name: Minghao Liu
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paper_authors.name: Xinyao Niu
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paper_authors.fullname: Xinyao Niu
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paper_authors.user: sirius-ctrl
paper_authors._id: 6721abd9c8acb97a181c367b
paper_authors.name: Yue Wang
paper_authors._id: 6721abd9c8acb97a181c367c
paper_authors.name: Jian Yang
paper_authors._id: 6721abd9c8acb97a181c367d
paper_authors.name: Jiaheng Liu
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paper_authors.fullname: Jiaheng Liu
paper_authors.type: user
paper_authors.user: CheeryLJH
paper_authors._id: 6721abd9c8acb97a181c367e
paper_authors.name: Wanjun Zhong
paper_authors._id: 6721abd9c8acb97a181c367f
paper_authors.name: Wangchunshu Zhou
paper_authors._id: 6721abd9c8acb97a181c3680
paper_authors.name: Wenhao Huang
paper_authors._id: 6721abd9c8acb97a181c3681
paper_authors.name: Ge Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-30T09:52:09.081Z
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paper_authors.avatarUrl: /avatars/97a57859d7d87a3a8f1bb41d32a72bc2.svg
paper_authors.fullname: Ge Zhang
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paper_authors.user: zhangysk
paper_title: AutoKaggle: A Multi-Agent Framework for Autonomous Data Science
    Competitions
paper_summary: 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.