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https://github.com/jainish-prajapati/solar-flare-prediction

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%.
https://github.com/jainish-prajapati/solar-flare-prediction

data-visualization machine-learning numpy pandas python scikit-learn solar-flare-prediction

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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%.

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README 

        
# Solar Flare Prediction – RHESSI Mission



## Overview



This project focuses on predicting solar flare energy ranges using machine learning techniques, developed for the NASA Space Apps Challenge 2024 by **Team Arjuna**. Solar flares can have significant impacts on Earth's space environment, and accurate predictions are crucial for mitigating their effects. 



For more information about our team, visit our page [here](https://www.spaceappschallenge.org/nasa-space-apps-2024/find-a-team/arjuna/?tab=members).



## Data Preprocessing



The dataset used in this project was obtained from the **NASA RHESSI Data Repository** and underwent several preprocessing steps to prepare it for analysis and modeling. The key steps include:



1. **Data Source**: The dataset was sourced from the NASA RHESSI Data Repository, accessible via a web form on [this page](https://hesperia.gsfc.nasa.gov/rhessi_data_search/rhessi_data_search_vme.html).



2. **FITS to CSV Conversion**: The original data files were in **FITS** format, which were converted to **CSV** format using tools available [here](https://fits.gsfc.nasa.gov/fits_viewer.html).



3. **Dataset Compilation**: Multiple CSV files generated from the FITS files were concatenated to create the current dataset. The script used to perform this concatenation can be found in this [repository](https://github.com/Jainish-Prajapati/Solar-Flare-Prediction/tree/main/Join_script).



The complete dataset is available in the repository [here](https://github.com/Jainish-Prajapati/Solar-Flare-Prediction/tree/main/data).



## Project Breakdown



### Analysis



The data analysis phase involved:

- Understanding the characteristics of solar flares, including their **energy distribution**, **duration**, and **occurrence over time**.

- Identifying patterns in the dataset to guide the feature selection for predictive modeling.



### Prediction



Various machine learning models were built and trained to predict the energy range of a solar flare based on its attributes. The models include:

- **Linear Regression**

- **Logistic Regression**

- **Decision Tree**

- **Random Forest**

- **K-Nearest Neighbors (KNN)**

- **Gradient Boosting**

- **Neural Network**



### Model Performance



The top three machine learning models, ranked by accuracy, are as follows:



1. **Gradient Boosting Classifier** – Achieved an accuracy of **87%**

2. **Random Forest Classifier** – Achieved an accuracy of **86%**

3. **Decision Tree Classifier** – Achieved an accuracy of **82%**



### Evaluation



The models were evaluated using several performance metrics:

- **Accuracy**

- **Precision**

- **Recall**

- **F1-score**



These metrics helped determine the effectiveness of each model in predicting the energy range of solar flares.



### Findings



- The **Random Forest** and **Gradient Boosting** models achieved the highest accuracy in predicting the energy range of solar flares.

- The project's findings contribute to the field of space weather research by providing a potential tool for predicting solar flares, which is critical for understanding and mitigating their impact on Earth.



## Conclusion



This project showcases the application of machine learning to space weather research, with a focus on predicting solar flares. The success of the **Gradient Boosting** and **Random Forest** models highlights their potential as robust tools in the field. Through this project, our team, **Arjuna**, aims to contribute to the advancement of solar flare prediction capabilities.



## Acknowledgments



- This project was developed as part of the **NASA Space Apps Challenge 2024** by **Team Arjuna**.

- Special thanks to the **NASA RHESSI Data Repository** for providing the data used in this analysis.



## Team Information



To learn more about **Team Arjuna** and its members, please visit our team page [here](https://www.spaceappschallenge.org/nasa-space-apps-2024/find-a-team/arjuna/?tab=members).



## References



- [NASA RHESSI Data Repository](https://hesperia.gsfc.nasa.gov/rhessi_data_search/rhessi_data_search_vme.html)

- [FITS Viewer and Tools](https://fits.gsfc.nasa.gov/fits_viewer.html)

- [Dataset Compilation Script](https://github.com/Jainish-Prajapati/Solar-Flare-Prediction/tree/main/Join_script)

- [Complete Dataset](https://github.com/Jainish-Prajapati/Solar-Flare-Prediction/tree/main/data)