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Ryan Orban

Ryan Orban

Subject
6 entries

Kaggle

Bookmarks

  1. Learning to Rank for Personalised Search (Yandex Kaggle Competition)

    Yanir Seroussi's Kaggle competition post-mortem on Yandex Search Personalisation — applying learning-to-rank techniques to personalized search with behavioral signals. A practical case study of LTR on real search logs.

  2. Approaching (Almost) Any Machine Learning Problem

    Abhishek Thakur's systematic framework for tackling any supervised ML problem — from data cleaning and feature engineering through model selection and stacking. One of the most-shared practical ML workflow guides from the Kaggle blog era.

  3. Machine Learning's First Cheating Scandal

    A post-mortem on a Kaggle-era incident where participants gamed the public leaderboard through repeated test-set probing — effectively overfitting to held-out data via the submission API. Raised serious questions about how ML benchmarks and competitions should be designed.

  4. My Solution for the Galaxy Zoo Challenge — Sander Dieleman

    Sander Dieleman's winning solution for the Galaxy Zoo Kaggle challenge using convolutional neural networks to classify galaxy morphology from images. A landmark result showing CNNs achieving human-level performance on a citizen science dataset.

  5. Weather Forecasting with Twitter and Pandas

    ŷhat blog post using Twitter emoticon sentiment as a proxy signal for weather prediction, analyzed with pandas. An early example of using social media signals for real-world forecasting — creative but ultimately a data exploration exercise.

  6. Getting Started With Python For Data Science (Kaggle)

    Kaggle's Getting Started With Python For Data Science guide — a practical on-ramp covering the core libraries (NumPy, pandas, matplotlib, scikit-learn) oriented around Kaggle competition workflows. The canonical starting point for competition-driven ML learning.

All bookmarks