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.

翻译：涉及表格数据的数据科学任务呈现出复杂的挑战，需要采用精细的问题解决方法。我们提出AutoKaggle，一个强大且以用户为中心的框架，通过协作式多智能体系统协助数据科学家完成日常数据流水线。AutoKaggle实现了结合代码执行、调试与全面单元测试的迭代开发流程，以确保代码正确性与逻辑一致性。该框架提供高度可定制的工作流，允许用户在每一阶段进行干预，从而将自动化智能与人类专业知识相融合。我们的通用数据科学工具包——包含数据清洗、特征工程和建模的已验证函数——构成了该解决方案的基础，通过简化常见任务提升了生产效率。我们选取了8个Kaggle竞赛来模拟实际应用场景中的数据处理工作流。评估结果表明，AutoKaggle在典型数据科学流水线中实现了0.85的验证提交率和0.82的综合评分，充分证明了其在处理复杂数据科学任务中的有效性与实用性。