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Exoplanet Detection Using Machine Learning
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: https://doi.org/10.64388/IREV9I8-1714036
Abstract
The detection of exoplanets plays a crucial role in understanding planetary systems beyond our solar system. Traditional detection techniques often require extensive manual verification, making automated solutions desirable. In this study, a machine learning–based approach for exoplanet detection is proposed using data from the NASA Kepler mission. After preprocessing and feature selection, a Random Forest classifier is trained to distinguish confirmed exoplanets from false positives. Experimental results demonstrate that the proposed model achieves an accuracy of 99.29
How to cite this paper
Saachi Sawant "Exoplanet Detection Using Machine Learning" Iconic Research And Engineering Journals Volume 9 Issue 8 2026 Page 87-93 https://doi.org/10.64388/IREV9I8-1714036
Saachi Sawant "Exoplanet Detection Using Machine Learning" Iconic Research And Engineering Journals, vol. 9, no. 8, Feb. 2026, doi: https://doi.org/10.64388/IREV9I8-1714036
Saachi Sawant (2026). Exoplanet Detection Using Machine Learning. Iconic Research And Engineering Journals, 9(8). doi: https://doi.org/10.64388/IREV9I8-1714036
Saachi Sawant "Exoplanet Detection Using Machine Learning" Iconic Research And Engineering Journals, vol. 9, no. 8, Feb. 2026. Crossref, https://doi.org/10.64388/IREV9I8-1714036
@article{1714036,
author = {Saachi Sawant},
title = {Exoplanet Detection Using Machine Learning},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {8},
pages = {87-93},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1714036.pdf},
abstract = {The detection of exoplanets plays a crucial role in understanding planetary systems beyond our solar system. Traditional detection techniques often require extensive manual verification, making automated solutions desirable. In this study, a machine learning–based approach for exoplanet detection is proposed using data from the NASA Kepler mission. After preprocessing and feature selection, a Random Forest classifier is trained to distinguish confirmed exoplanets from false positives. Experimental results demonstrate that the proposed model achieves an accuracy of 99.29},
month = {February},
doi = {https://doi.org/10.64388/IREV9I8-1714036}
}