Nowadays, there is a very adverse impact on economic, cultural, social and almost all fields in the world because of Covid-19. The Covid-19 term is described as -`CO` for corona, `VI` for virus, and `D` for disease. It is an infectious disease caused by severe acute respiratory syndrome which is transmitted through respiratory droplets and contact routes. Since December 2019, corona-virus disease (COVID-19) has out-broke from the country China. Till now, more than 78, 23, 289 people are infected and more than 4 Lakhs of deaths have been caused worldwide. Unfortunately, the number of infections and deaths are still increasing rapidly which has put the world in a different state. Artificial Intelligence can play a key role to infection forecasting in national and provincial levels in many countries. The objective of this study is to use machine learning methods to forecast the number of cases for the next 2 weeks, i.e. till 30th June 2020. The data was collected from 22nd January to 15th June 2020 by nationally recognized sources. The data file contains the cumulative count of confirmed, death and recovered cases of COVID-19 from different countries from the date 22nd January 2020.In this study, the outbreak of this disease has been analyzed for India till 15th June 2020 and predictions have been made for the number of cases for the next two weeks.

Covid-19, Corona, Corona Virus, Machine Learning, Forecasting, Artificial Intelligence, time series forecasting

[1] R.S. Walse, G.D. Kurundkar, P. U. Bhalchandra, "A Review: Design and Development of Novel Techniques for Clustering and Classification of Data," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.19-22, 2018
[2] Hemant Kumar Soni, "Machine Learning – A New Paradigm of AI," International Journal of Scientific Research in Network Security and Communication, Vol.7, Issue.3, pp.31-32, 2019
[3] Amogha A.K., "Load Forecasting Algorithms with Simulation & Coding," International Journal of Scientific Research in Network Security and Communication, Vol.7, Issue.2, pp.15-20, 2019
[4] K. Krishna Rani Samal, Korra Sathya Babu, Santosh Kumar Das, Abhirup Acharaya , ?Time Series based Air Pollution Forecasting using SARIMA and Prophet Model? , In the Proceedings of ITCC 2019: International Conference on Information Technology and Computer Communications, pp 80-85, 2019.
[5] Upendra Kumar Tiwari & Rizwan Khan, ? Role of Machine Learning to Predict the Outbreak of Covid-19 in India?, Journal of Xi`an University of Architecture & Technology, Vol.12, Issue.4, pp. 2663-2669, 2020.
[6] Herlawati ,?COVID-19 Spread Pattern Using Support Vector Regression?, PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Journal , Vol.8, Issue.1, pp. 67-74, 2020
[7] Dutta,Shawni , Samir Kumar Bandyopadhyay ,Tai-Hoon kim , ?CNN-LSTM Model for Verifying Predictions of Covid-19 Cases?, Asian Journal of Computer Science and Information Technology, Vol.5, Issue.4, pp. 25-32, 2020
[8] Rustam, Furqan & Reshi, Aijaz & Mehmood, Arif & Ullah, Dr. Saleem & On, Byungwon & Aslam, Waqar & Choi, Gyu Sang, ?COVID-19 Future Forecasting Using Supervised Machine Learning Models?, in IEEE Access, Vol. 8, pp. 101489-101499, 2020
[9] R. Ranjan, ?Predictions for COVID-19 outbreak in India using Epidemiological models? medRxiv 10.1101/2020.04.02.20051466, 2020
[10] Simon James Fong, Gloria Li, Nilanjan Dey, Rub?n Gonz?lez Crespo, Enrique Herrera-Viedma, ? Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak?, International Journal of Interactive Multimedia and Artificial Intelligence, Vol.6, Issue.1, pp. 132-140 , 2020
[11] Petropoulos F, Makridakis S, ?Forecasting the novel coronavirus COVID-19? , PLOS ONE journal , March 31, 2020. https://doi.org/10.1371/journal.pone.0231236 , 2020
[12] Zheng N, Du S, Wang J, Zhang H, Cui W, Kang Z, et al. , ?Predicting COVID-19 in China Using Hybrid AI Model?, IEEE Trans Cybern, https://doi.org/10.1109/TCYB.2020.2990162, 2020
[13] Heni Bouhamed, "Covid-19 Cases and Recovery Previsions with Deep Learning Nested Sequence Prediction Models with Long Short-Term Memory (LSTM) Architecture," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.2, pp.10-15, 2020