A machine learning project leveraging LSTM, GRU, and Bidirectional LSTM + GRU models for accurate prediction of solar flare peak current per second (c/s) and energy magnitude, with comprehensive hyperparameter tuning for optimal performance

This repository contains code and data for predicting solar flare energy ranges using machine learning, based on NASA's RHESSI mission data. It includes preprocessing of FITS files into a unified CSV dataset and implements models like Gradient Boosting, Random Forest, and Decision Tree classifiers, achieving accuracies up to 87%.

CONTREX, a novel contrastive representation learning approach for multivariate time series data.