Machine learning and deep learning play vital roles in predicting diseases in the medical field. Machine learning algorithms are widely classified as supervised, unsupervised, and reinforcement learning. This paper contains a detailed description of our experimental research work in that we used a supervised machine-learning algorithm to build our model for outbreaks of the novel Coronavirus that has spread over the whole world and caused many deaths, which is one of the most disastrous Pandemics in the history of the world. The people suffered physically and economically to survive in this lockdown. This work aims to understand better how machine learning, ensemble, and deep learning models work and are implemented in the real dataset. In our work, we are going to analyze the current trend or pattern of the coronavirus and then predict the further future of the covid-19 confirmed cases or new cases by training the past Covid-19 dataset by using the machine learning algorithm such as Linear Regression, Polynomial Regression, K-nearest neighbor, Decision Tree, Support Vector Machine and Random forest algorithm are used to train the model. The decision tree and the Random Forest algorithm perform better than SVR in this work. The performance of SVR and lasso regression are low in all prediction areas Because the SVR is challenging to separate the data using the hyperplane for this type of problem. So SVR mostly gives a lower performance in this problem. Ensemble (Voting, Bagging, and Stacking) and deep learning models(ANN) also predict well. After the prediction, we evaluated the model using MAE, MSE, RMSE, and MAPE. This work aims to find the trend/pattern of the covid-19.

翻译：机器学习和深度学习在医疗领域的疾病预测中发挥着重要作用。机器学习算法广泛分类为监督学习、无监督学习和强化学习。本文详细描述了我们的实验研究工作，我们采用监督机器学习算法构建模型，以预测已蔓延至全球并导致大量死亡的新型冠状病毒疫情——这是世界历史上最具灾难性的流行病之一。人们在封锁期间在身体和经济上都遭受了痛苦。本研究旨在更深入地理解机器学习、集成学习和深度学习模型如何在实际数据集上工作及实现。在我们的工作中，我们将分析冠状病毒的当前趋势或模式，然后通过使用线性回归、多项式回归、K近邻、决策树、支持向量机和随机森林等机器学习算法训练过去的COVID-19数据集，预测COVID-19确诊病例或新增病例的未来趋势。研究表明，决策树和随机森林算法在此工作中的表现优于SVR。SVR和套索回归在所有预测区域中的性能较低，因为SVR难以针对此类问题使用超平面分离数据，因此其在多数情况下表现不佳。集成学习（投票、Bagging和Stacking）和深度学习模型（ANN）也表现出良好的预测能力。预测完成后，我们使用MAE、MSE、RMSE和MAPE评估模型。本研究旨在发现COVID-19的趋势/模式。