Analysis and Prediction of COVID-19 Disease using Machine Learning Shabnam Parmar 1 , Rinkle Rani 2 and Nidhi Kalra 3 1-3 Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India Email: sparmar_me20@thapar.edu, raggarwal@thapar.edu, nidhi.kalra@thapar.edu Abstract—In this research, the symptoms and other factors of a patient are utilized to train machine learning algorithms to predict whether the patient would die from or recover from (COVID19). It is probable that the coronavirus (COVID19) will create the highly infectious Coronavirus illness COVID19 (SARSCoV2). By coughing, sneezing, speaking, or inhaling, this virus can be transmitted from an infected person's lips and nose to small liquid particles. The size of these tiny atoms' inhalation droplets and aerosols vary, with bigger droplets being larger than smaller atoms. COVID19 is transferred by inhalation or by touching your eyes, nose, or mouth with your fingers after meeting a contaminated surface. When a big number of people are present, the COVID-19 virus can spread rapidly. We will need to examine the COVID19 dataset to see which models are the most accurate in estimating fatality rates for the virus's most vulnerable victims. Machine learning is used to compute and evaluate the performance of a variety of prediction models. We have used K-nearest neighbor (KNN), Support Vector Machine (SVM) classifier, Gaussian naive Bayesian (GNB), Decision tree (DT), and Logistic regression (LR) for the prediction of death and recovery of symptomatic patients. In this research, a variety of feature selection and extraction strategies were used, and prediction accuracy for feature selection methods for the KNN model and feature extraction methods for the GNB model both reached up to 96 percent. The k-nearest neighbor has performed and predicted high accuracy of 96% in both feature selection and extraction techniques. Index Terms— COVID19, Decision Trees, Gaussian Naive Bayesian, KNN, Logistic Regression, Machine Learning, SVM, PCA, Fast-ICA, K-Fold, Feature Selection, and Extraction. I. INTRODUCTION Recently, the globe has seen fast technological advancement, which demonstrates the critical role of progressive countries. Today, every aspect of society, including education, employment, trade, military, and media, as well as manufacturing and healthcare, is obsessed with continuing and emerging technological advancements. The centre may be a critical location for the rapid adoption of new technologies, ranging from diagnosis to accurate identification and automated analysis of patients. Coronavirus2 (SARSCoV2) source of severe inhaling pollution and anarchy, in resultant 2019 (COVID19) was identified in humans because of initial cases in the Chinese city of Wuhan in December 2019 [1]. Machine Learning is a promising categorization technology. In general, machine learning is a useful framework for inducing an unknown purpose, relationship, or structure between output and input variables. Generally, all these interactions are extremely difficult to handle using explicit models and machine learning, which is why they are mostly employed to anticipate or forecast the possible number of confirmed cases and hence the number Grenze ID: 01.GIJET.9.1.577 © Grenze Scientific Society, 2023 Grenze International Journal of Engineering and Technology, Jan Issue