kaggle work · shivam bhardwaj

Numbers,
strategy,
and the infra
to repeat both.

§ catalog · 5 entries
  • Computer Vision
  • Bioinformatics
  • Audio
  • Tabular
FILE 02 / Bioinformatics Active

Stanford RNA 3D Folding 2

Predict the 3D structure of RNA from sequence. A classical sequence-NN baseline beats anything that ignores the closest train neighbors; RibonanzaNet3D refines the candidates; a learned reranker picks the final five.

33.98 33.89 top-1 RMSD
5,716 train targets · MSAs up to 14k seqs · up to 125k nt read
FILE 03 / Audio In progress

BirdCLEF 2026

Acoustic species ID in Pantanal soundscapes — research-track Kaggle. Pipeline live with a class-prior baseline; deep audio modeling is next.

TBA per Kaggle eval
weak labels at recording level · dense per-window submission read
FILE 04 / Tabular Active

AI Adoption · Fortune 500

EDA + clustering on a synthetic Fortune-500 AI-adoption panel. Random Forest ROI predictor, K-means personas, PCA projection, correlation atlas, use-case trajectories.

Random Forest R² (runtime)
572 KB · 2020–2025 · Fortune 500 read
FILE 05 / Tabular Active

Student Performance EDA

EDA on student grades — Random Forest predictor of overall_score, behavioral K-means personas, sleep / study correlations, A→F grade distributions.

Random Forest R² (runtime)
792 KB · grades A · B · C · D · F read
FILE 06 / Computer Vision Active

Digit Recognizer

Legacy MNIST kept as the canonical scaffold — the smoke-test that proves challenge.json, the dashboard registry, the remote bootstrap, and the submit-to-Kaggle flow all still work.

accuracy
42k train · 28k test · 28×28 grayscale read