PREDICTING THE HABITABILITY OF EXOPLANETS USING STATISTICAL MODELING AND MACHINE LEARNING

The aim of this paper is to identify the potential of using Machine Learning algorithms to predict exoplanet habitability through publicly available datasets.An initial dataset of more than 5000 observations was analyzed using Logistic Regression and Random Forest classifiers.Out of the two Machine Learning models, Logistic Regression performed poorly due to high class imbalance whereas the Random Forest classifier achieved high accuracy with strong specificity and sensitivity.The dataset was later expanded to include more than 10,000 confirmed exoplanets, with the habitability parameter redefined via the Earth Similarity Index.This change modified the percentage of habitable exoplanets to decrease from around 4% to less than 0.7%.While the Random Forest model achieved perfect accuracy, potential overfitting was noted.