extratrees

deep learning and scientific computing framework with native CPU and GPU backend for the Scala programming language

sklearn, tensorflow, random-forest, adaboost, decision-tress, polynomial-regression, g-boost, knn, extratrees, svr, ridge, bayesian-ridge

the aim of this project is to develop a machine learning model to predict temperature and rainfall based on environmental features, with a specific focus on six air quality parameters identified by the World Health Organization (WHO).

Startups Outcome Prediction — ML Pipeline est une solution complète de Machine Learning développée en Python (Pandas, Scikit-learn, LightGBM, ExtraTrees) et déployée via une API Flask sur Render. Ce projet vise à prédire le statut futur de jeunes entreprises (en activité, en bourse, acquises ou fermées) en se basant sur des données historiques de

A project on predicting snow depth using L-Band InSAR parameters.

Real-time fraud detection system — ExtraTrees, F1=0.8962 on 284K transactions, FastAPI + Streamlit, SHAP explainability

PHRC-TAGS: voltage-only cross-capacity external-short-circuit detection for LiFePO4 cells, with frozen models, audits, and publication figures.

Data Science and Machine Learning using Python and R

DACON/ETRI ICTC code package for personalized digital bedtime routine modeling with ExtraTrees shrinkage

Stacked Ensemble Learning (ExtraTrees + XGBoost + HistGBM) for Residential Appliance Energy Prediction & Dual-Tier Anomaly Screening (R²=0.7504) with Interactive Web Dashboard and REST API.

Predict product failures

End-to-end machine-learning project that predicts telecom customer-churn with 81.5 % accuracy: cleans data, benchmarks Random Forest / XGBoost / LightGBM, tunes ExtraTrees via RandomizedSearchCV, and ranks the features that matter most.