The NASA Prognostic Model Package is a Python framework focused on defining and building models for prognostics (computation of remaining useful life) of engineering systems, and provides a set of prognostics models for select components developed within this framework, suitable for use in prognostics applications for these components.

The NASA Prognostic Python Packages is a Python framework focused on defining and building models and algorit for prognostics (computation of remaining useful life) of engineering systems, and provides a set of models and algorithms for select components developed within this framework, suitable for use in prognostic applications.

This is the official repository of the R package metamisc

Reproduce results from the paper "Development, validation and clinical usefulness of a prognostic model for relapse in relapsing-remitting multiple sclerosis. K. Chalkou et. al. Diagn Progn Res . 2021 Oct 27;5(1):17. doi: 10.1186/s41512-021-00106-6."

I wanted to use a neural network built with TensorFlow and Keras to predict future real estate prices based on property attributes like size, location, and market trends. Users can input details interactively, and the model provides a price prediction along with a 12-month forecast visualization.

Time-adjusted effect of socioeconomic status in mortality rates after brain injury: cohort study

Sensitivity of mortality rates to the imputation of missing socioeconomic data: cohort study

Effect of socioeconomic status in mortality rates after brain injury: cohort study

NASA Turbofan Jet Engine Propagation modeling

Conceptual models of oceanic diurnal warm layer dynamics

Anomaly Detection in Time Series Data using Autoencoders approach.

Implant failure rates in a knee prosthesis sub-population of the Helios Klinikum Berlin-Buch hospitals

Prognóstico de sobrevida em cativeiro de Tityus bahiensis capturados em Americana/SP

Prognóstico de componentes hematológicos após ATQ bilateral simultânea em centro cirúrgico de referência

This repo contains the code used for the CARSS external validation project.

Benchmarking ML models for predicting DT Cancer recurrence with nested CV and explaining predictions with SHAP (XAI).

Consultorias em Estatística Médica e Epidemiologia Clínica. CNPJ:42.154.074/0001-22