Hazardous Asteroid Prediction using Majority Voting Technique

The existence of life on the earth not only depends upon trees, water, food, minerals, and other resources of the earth, but also upon the asteroids and objects that travel from the outer atmosphere of the earth. When an asteroid from space, with very high speed, hits the earth’s surface [Meteorite], it deals a lot of damage to the human beings and this planet. The goal of this research study is to build one such machine learning model which predicts the Hazardous asteroid effectively. In this work, six ML models namely Naive Bayes, Logistic Regression, Decision Tree, Random Forest, K-NN and SVM were used for hazardous asteroid prediction. Based on accuracy, Random Forest, Logistic Regression and Decision Tree were combined using Majority Voting Technique to predict whether the asteroid is hazardous 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 90% to 99.86% and MVT produced 100% accuracy.