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Save thousands of API calls. Custom model & dataset aiming at predicting a game difficulty score ("lobby avg kd") without calling players' games history stats and profiles.

Home Page: https://warzone1-kd.streamlit.app

Python 8.69% Jupyter Notebook 91.31%
api callofduty feature-engineering feature-extraction graphql scraping shapley xgboost

match2kd's Introduction

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Ex. data scientist @Linkfluence (Radarly). NLP, tabular data, time-series, APIs, but curious about anything, really.


Current projects : NLP, LLM fine-tuning / deployment optimization :
  • lmd_classi : Classifier pro-russian comments. From (small) annotated data + baseline w/ few-shot (SetFit) to synthetic data generation, LLM (Mistral-7B or LLama3-8B) fine-tuning and multi-e5-base classifier w/ quantization for deployment. Full guide, ressources, benchmarks, available as organized notebooks.
  • lmd_viz : Ukraine War (1st year) : comments as a proxy for people engagement. Viz + crafted a 200k dataset from Le Monde w/ my own API (usable "as is").

Cool stuff :

  • wzkd app & match2kd : reverse engineering matchmaking score from players' features only ; using XGB & streamlit App.
  • wzlight : light wrapper around COD WZ api (discontinuated since). Also available on PyPi.

My favorite AI/ML newsletter (should read it too!) :

Data Machina : not mine, not sponsored. Technical-but-simple enough and existed way before the LLM hype.

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