Exoplanet Detection Using Feature Engineering with Ensemble Learning

In this work, a novel technique for detecting extra-terrestrial bodies using the transit method, with the aim of improving traditional algorithmic strategies in astronomy through machine learning algorithms. The potential for this approach to revolutionize the field of exoplanet detection and unlock new insights into the universe is highlighted. An ensemble learning approach using maj ority voting technique is employed to detect Exoplanets. In this work, we have used six machine learning models Random Forest, Decision Tree, Support Vector Classifier, K-Nearest Neighbor and Multi- Layer Perceptron, for Exoplanet Detection. Based on accuracy, we combined Support vector Classifier, K-Nearest Neighbor, Random Forest and Multi-Layer Perceptron using Majority Voting Technique to predict whether the planet is exoplanet or not. Majority Voting Technique (MVT) of machine learning models has shown higher significance when compared to the individual machine learning model in terms of accuracy. The accuracy of different individual models is ranging from 92.59% to 99.88% and MVT produced 99.97% accuracy.