• Thumma Mary Shreeja
• Mora Srujana
• Chilla Sneha
• K. Murali

This project is titled as “Corona Virus Infection Probability Classification”. Due to the unexpected outbreak of COVID-19 disease, the world is facing a major epidemic in current days. The infection as well as the death rate is growing rapidly in every country. The world economic status is also decreasing due to this disaster. It is more essential to detect the infected people at an early stage to make a break in spreading of virus. Machine learning techniques will be very useful for this purpose due to its automatic data analysis and classification ability. In the proposed work, authors have classified samples having chance of infection. A set of randomly generated data is considered for the classification purpose. The dataset contains 1200 samples with five types of COVID symptoms. By analyzing the body temperature, age, body pain, runny nose status, and breathing problem. Based on experiment result on data set, found best algorithm among the three classifiers: Decision tree, SVM, Naive Bayes, Neural Network, CNN, and Random Forest.

SVM, Naive Bayes, Neural Network, Decision Tree, Random Forest, CNN.

"CORONA VIRUS INFECTION PROBABILITY CLASSIFICATION USING MACHINE LEARNING ALGORITHMS.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.b578-b583, June-2021, Available :http://www.jetir.org/papers/JETIR2106221.pdf

"CORONA VIRUS INFECTION PROBABILITY CLASSIFICATION USING MACHINE LEARNING ALGORITHMS.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppb578-b583, June-2021, Available at : http://www.jetir.org/papers/JETIR2106221.pdf