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).

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

Predict product failures

Data Science and Machine Learning using Python and R

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.