Winning a Kaggle Competition with Generative AI–Assisted Coding

2026-04-23 · Source: NVIDIA Technical Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

In March 2026, three LLM agents—GPT-5.4 Pro, Gemini 3.1 Pro, and Claude Opus 4.6—generated over 600,000 lines of code and ran 850 experiments to secure a first-place finish in a Kaggle Playground churn prediction competition. This achievement highlights how LLM agents, combined with GPU acceleration, significantly compress the iteration loop for machine learning experimentation by addressing the bottleneck of code generation. The winning solution was a four-level stack of 150 models, selected from 850, developed using a human-in-the-loop workflow. The process involved LLM agents performing exploratory data analysis (EDA), building baseline models, conducting feature engineering, and finally combining models through hill climbing and stacking, leveraging GPU libraries like NVIDIA cuDF, cuML, XGBoost, and PyTorch for rapid execution.

Key takeaway

For Data Scientists and ML Engineers aiming to accelerate tabular data prediction tasks, integrating LLM agents into your workflow can significantly boost experimentation speed. By having agents generate code for EDA, baselines, and feature engineering, and then combine models, you can explore hundreds of solutions rapidly. Leverage GPU-accelerated libraries like cuDF and cuML to ensure fast execution, transforming your iterative development cycle and potentially improving model performance.

Key insights

LLM agents, paired with GPU acceleration, dramatically speed up ML experimentation by automating code generation and execution.

Principles

• Rapid iteration is key to ML competition success.
• Combine LLM agents with GPU libraries for speed.
• A human-in-the-loop workflow optimizes agent performance.

Method

The guided LLM agent workflow for tabular data involves EDA, baseline model building, feature engineering, and model combination via hill climbing and stacking, using GPU-accelerated libraries.

In practice

• Use LLMs for EDA code generation.
• Prompt LLMs to build kfold XGBoost baselines.
• Employ LLMs to generate feature engineering ideas.

Topics

• Generative AI-Assisted Coding
• LLM Agents
• Kaggle Competitions
• Tabular Data Prediction
• Feature Engineering

Best for: Machine Learning Engineer, Data Scientist, AI Engineer

Related on AIssential

• AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning — LLMs effectively guide compiler optimization by integrating compiler-internal signals and runtime feedback without offline training.
• Self-Evolving Deep Research via Joint Generation and Evaluation — Co-evolving LLM generation and evaluation within a shared-parameter framework overcomes static evaluator limitations in deep research.
• Explainable Iterative Data Visualisation Refinement via an LLM Agent — An LLM-driven agent automates and explains data visualization refinement by iteratively optimizing DR hyperparameters.
• CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation — Agentic reinforcement learning can significantly enhance LLMs' intrinsic CUDA kernel optimization capabilities.
• Programming with LLM Agents in 2025 — LLM agents, like Open Hands, significantly amplify developer productivity by automating iterative coding and visualization tasks.
• Exploring LLM-based Verilog Code Generation with Data-Efficient Fine-Tuning and Testbench Automation — Multi-agent LLMs can automate testbench generation, creating high-quality data for efficient Verilog code generation.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by NVIDIA Technical Blog.