A detailed analysis of Covid-19 using supervised machine learning models

Machine learning (ML) prediction Algorithms have exemplified their capacity to foresee perioperative results in order to enhance prospective course of action decisions. In many application domains, the ML models have long been used which needed to recognise and prioritise adverse threats. A number of tools for statistical problems and data comprehension are also used to address decision-making. This study demonstrates the ability of ML models to predict the number of future patients affected by COVID-19 who are currently considered to be a abeyant intimidation to hominids. Three standard models, such as linear regression (LR), Decision tree regressor(DR), Random forest regressor(RFR). LR models make Three types of predictions, such as the amount of newly infected cases and the amount of recoveries, Amount of Deaths based on COVID-19 tests on each phase. The study divides into three stages(peak, transition, slowdown). DR and RFR help to understand insights of data. Results show the unlockdown causes for rapid increase in Covid infection rate, 78% deaths happen due to covid-19 in peak stage only. Andhra Pradesh’s average infection rate is 16.58% near the ICMR Third Sero Sample Survey. In Andhra Pradesh, testing is a primary parameter as opposed to population density. Chittoor, Guntur, Krishna, Nellore, Visakhapatnam Are The Five Host Spot Districts For Covid-19 Spreading In Andhra Pradesh State.