Abhishek Thakur Portfolio
World's first 4x Kaggle Grandmaster (Competitions, Datasets, Notebooks, Discussion) and author of "Approaching (Almost) Any Machine Learning Problem" — his Kaggle profile is a fully public, ranked, adjudicated record of hundreds of real modeling problems solved.
What makes it work
A breakdown of the choices that make this portfolio stand out.
Adjudicated, ranked results across four separate categories
Grandmaster status across competitions, datasets, notebooks, and discussion is earned through independent, judged outcomes against a global pool — the closest thing to an unfakeable leaderboard.
Public book and open-source tooling built from the same practice
His book and open-source tooling are the codified, teachable version of what his notebooks show ad hoc — proof he generalizes hands-on skill into reusable process.
Volume and consistency of public notebooks
A multi-year, still-active stream of ranked notebooks demonstrates sustained output, not a one-time portfolio push before a job search.
What Data Scientists can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Compete on Kaggle even outside your specialty — placements are judged by an outside standard no self-graded project can match.
Publish notebooks with clear methodology write-ups, not just winning code.
Turn repeated ad hoc techniques into a reusable tool or package once you have done them enough times.
Treat Kaggle rank, GitHub stars, and publications as three independent forms of proof — collecting more than one type compounds credibility.
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