The proposed work utilizes support vector regression model to predict the number of total number of deaths, recovered cases, cumulative number of confirmed cases and number of daily cases. The data is collected for the time period of 1^st March,2020 to 30^th April,2020 (61 Days). The total number of cases as on 30^th April is found to be 35043 confirmed cases with 1147 total deaths and 8889 recovered patients. The model has been developed in Python 3.6.3 to obtain the predicted values of aforementioned cases till 30^th June,2020. The proposed methodology is based on prediction of values using support vector regression model with Radial Basis Function as the kernel and 10% confidence interval for the curve fitting. The data has been split into train and test set with test size 40% and training 60%. The model performance parameters are calculated as mean square error, root mean square error, regression score and percentage accuracy. The model has above 97% accuracy in predicting deaths, recovered, cumulative number of confirmed cases and 87% accuracy in predicting daily new cases. The results suggest a Gaussian decrease of the number of cases and could take another 3 to 4 months to come down the minimum level with no new cases being reported. The method is very efficient and has higher accuracy than linear or polynomial regression.

拟议的工作利用支持向量回归模型来预测死亡总数，康复病例数，确诊病例累积数和每日病例数。收集的数据为2020年3月1^日至2020年4月30^日（61天）。截至4月30^日，确诊病例总数为35043例，总死亡1147例，康复患者8889例。该模型已在Python 3.6.3中开发，以获得上述案例的预测值，直到30^日2020年6月。所提出的方法是基于使用支持向量回归模型对值进行预测的，其中支持向量回归模型以径向基函数为核心，曲线拟合的置信区间为10％。数据已分为训练量和测试集，测试量为40％，训练量为60％。模型性能参数计算为均方误差，均方根误差，回归得分和百分比准确性。该模型在预测死亡，康复，已确诊病例的累计数量方面的准确性超过97％，在预测每日新病例方面的准确性超过87％。结果表明，病例数呈高斯下降趋势，可能还需要3到4个月才能降至最低水平，而没有新病例报告。该方法非常有效，比线性或多项式回归具有更高的准确性。