Coronavirus disease (COVID-19) is an inflammation disease from a new virus. The disease causes respiratory ailment (like influenza) with manifestations, for example, cold, cough and fever, and in progressively serious cases, the problem in breathing. COVID-2019 has been perceived as a worldwide pandemic and a few examinations are being led utilizing different numerical models to anticipate the likely advancement of this pestilence. These numerical models dependent on different factors and investigations are dependent upon potential inclination. Here, we presented a model that could be useful to predict the spread of COVID-2019. We have performed linear regression, Multilayer perceptron and Vector autoregression method for desire on the COVID-19 Kaggle data to anticipate the epidemiological example of the ailment and pace of COVID-2019 cases in India. Anticipated the potential patterns of COVID-19 effects in India dependent on data gathered from Kaggle. With the common data about confirmed, death and recovered cases across India for over the time length helps in anticipating and estimating the not so distant future. For extra assessment or future perspective, case definition and data combination must be kept up persistently.

冠状病毒病 (COVID-19) 是一种新病毒引起的炎症性疾病。这种疾病会引起呼吸道疾病（如流感），表现为感冒、咳嗽和发烧，在逐渐严重的情况下，还会出现呼吸问题。COVID-2019 已被视为全球流行病，并且正在使用不同的数值模型进行一些检查，以预测这种瘟疫的可能发展。这些数值模型取决于不同的因素，而调查取决于潜在的倾向。在这里，我们提出了一个模型，该模型可用于预测 COVID-2019 的传播。我们进行了线性回归，多层感知器和向量自回归方法对 COVID-19 Kaggle 数据的期望，以预测印度 COVID-2019 病例的疾病和速度的流行病学示例。根据从 Kaggle 收集的数据，预测了印度 COVID-19 影响的潜在模式。借助有关印度各地确诊、死亡和康复病例的共同数据，在一段时间内有助于预测和估计不远的未来。对于额外的评估或未来的展望，必须持续保持案例定义和数据组合。