Prediction of Covid-19 pandemic based on Regression
Ashish U Mandayam, Rakshith A.C, S Siddesha, S. K. Niranjan
Abstract
With the progression in the field of machine learning, predictive analysis has become a key component for future prediction. As we face the COVID-19 pandemic, it would be helpful to predict the future number of positive cases for better measures and control. We used two supervised learning models to predict the future using the time-series dataset of COVID-19. To study the performance of prediction, the comparison between Linear Regression and Support Vector Regression is carried out. We have used these two models as the data were almost linear.
Topics & Concepts
Coronavirus disease 2019 (COVID-19)Computer scienceSupport vector machineRegressionArtificial intelligenceMachine learningRegression analysisField (mathematics)Linear regressionPredictive modellingPandemicTime seriesSupervised learning2019-20 coronavirus outbreakStatisticsArtificial neural networkMathematicsMedicineDiseasePathologyVirologyPure mathematicsInfectious disease (medical specialty)OutbreakAnomaly Detection Techniques and ApplicationsCOVID-19 diagnosis using AICOVID-19 epidemiological studies