MACHINE LEARNING PREDICTION FOR COVID 19 PANDEMIC IN INDIA

Ogundokun, R. O.; Awotunde, J. B.

Background: Coronavirus was detected in December 2019 in a bulk seafood shop in Wuhan, China. The original incident of COVID-19 pandemic in India was conveyed on 30th January 2020 instigating from the nation called china. As of 25th April 2020, the Ministry of Health and Family Welfare has established a total of 24, 942 incidents, 5, 210 recuperation including 1 relocation, and 779 demises in the republic. Objective: The objective of the paper is to formulate a simple average aggregated machine learning method to predict the number, size, and length of COVID-19 cases extent and wind-up period crosswise India. Method: This study examined the datasets via the Autoregressive Integrated Moving Average Model (ARIMA). The study also built a simple mean aggregated method established on the performance of 3 regression techniques such as Support Vector Regression (SVR, NN, and LR), Neural Network, and Linear Regression. Result: The results showed that COVID-19 disease can correctly be predicted. The result of the prediction shows that COVID-19 ailment could be conveyed through water and air ecological variables and so preventives measures such as social distancing, wearing of mask and hand gloves, staying at home can help to avert the circulation of the sickness thereby resulting in reduced active cases and even mortality. Conclusion: It was established that the projected method outperformed when likened to previously obtainable practical models on the bases of prediction precision. Hence, putting in place the preventive measures can effectively manage the spread of COVID-19, and also the death rate will be reduced and eventually be over in India and other nations.