Utilizing GDAL to georeference Images in a citizen science project

Interactive asteroid impact simulator using real NASA data and physics-based calculations to demonstrate planetary defense capabilities.

A machine learning project for predictive maintenance of turbofan engines, featuring a Flask web application with visualizations, model predictions, and deployment via Docker. Includes datasets FD001-FD004 from the NASA Prognostics Data Repository.

Several Python and R scripts, notebooks... that might be useful when studying Data Science

Machine learning regression models for predicting battery Remaining Useful Life (RUL) and capacity degradation using NASA battery aging dataset.

Architectural analysis of Convolutional Neural Networks (CNN) for Mars surface classification. Built for the AREP course at Escuela Colombiana de Ingeniería Julio Garavito — featuring comparative experiments between CNNs and Dense networks, inductive bias analysis, and AWS SageMaker execution.

This project focuses on anomaly detection in multivariate time series from NASA spacecraft telemetry (SMAP and MSL). It provides a modular and reproducible framework for data exploration, anomaly label processing, visualization, and future integration of detection models.

Predict the Remaining Useful Life (RUL) of aircraft engines using NASA's Turbofan Engine Degradation Simulation Data. Leverage machine learning for predictive maintenance to reduce costs and prevent failures.

Production-style machine learning system with deployable architecture for predictive maintenance using NASA turbofan data. Features XGBoost/RF classifiers, RL scheduler, and FastAPI deployment.

Industrial equipment anomaly detection using NASA bearing dataset. Production ML pipeline with FastAPI, Docker, and comprehensive testing.

Predictive Maintenance system utilizing XGBoost to estimate the Remaining Useful Life (RUL) of NASA Turbofan Engines (CMAPSS dataset) to prevent failures and optimize maintenance schedules.