# Study Past Kaggle Winners' Solution Writeups

> How to find and learn from winning Kaggle competition solution write-ups.

A practical method for finding high-performing machine learning strategies directly from top Kaggle competitors. After competitions end, podium finishers publish detailed post-mortems covering their cross-validation setups, model architectures, and failed experiments. Reviewing these write-ups provides proven recipes for tabular, vision, and NLP problems without trial and error.

## How it works

How to access winning Kaggle strategies:

1. Go to kaggle.com and open the Competitions section.
2. Scroll down to 'Recently Completed' competitions and select one in your target domain (computer vision, tabular, NLP, time series, etc.).
3. Click on the 'Leaderboard' tab.
4. Look at the rightmost column ('Solution') for the document icon next to the top 10–20 ranking teams.
5. Click the document icon to read the team's solution write-up in the Discussion section.
6. Study their cross-validation setups, model architectures (e.g. GBDTs, neural network backbones), data augmentation or synthetic data generation, hyperparameter choices, and their 'Didn't work' sections to avoid dead ends in active competitions.

## What it is for

- Design effective cross-validation schemes tailored to specific data distributions
- Identify top-performing model architectures and ensembling strategies for a competition domain
- Avoid dead-end preprocessing and modeling approaches by reviewing what did not work for winners

## Key points

- Navigate to Kaggle Competitions and filter by completed events
- Inspect the final Leaderboard tab for top-ranking teams
- Click the document icon in the Solution column to open write-ups
- Analyze documented validation strategies and correlation with the leaderboard
- Review feature engineering, data augmentations, and neural backbones used
- Examine 'What didn't work' sections to save development time

## Pricing

Free to read and access on Kaggle

## Details

- Type: Tip
- Category: [Learning](https://xiry.app/topics/learning.md)
- Open source: no
- Platforms: Web
- For: Competitive data scientists and machine learning engineers looking to improve model performance
- Tags: kaggle, machine learning, data science, competitions, learning

## Why it was saved

- [@softwarewithnick, 27 October 2024](https://www.instagram.com/reel/DBm8MBPvL8g/): Top competitors write extensive post-mortems detailing their exact cross-validation strategies, feature engineering, models, hyperparameters, and what didn't work, saving you months of trial and error.

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Updated 3 October 2026. Page: https://xiry.app/resources/study-past-kaggle-winners-solution-writeups

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