Pandemic novel Coronavirus (Covid‐19) is an infectious disease that primarily spreads by droplets of nose discharge when sneezing and saliva from the mouth when coughing, that had first been reported in Wuhan, China in December 2019. Covid‐19 became a global pandemic, which led to a harmful impact on the world. Many predictive models of Covid‐19 are being proposed by academic researchers around the world to take the foremost decisions and enforce the appropriate control measures. Due to the lack of accurate Covid‐19 records and uncertainty, the standard techniques are being failed to correctly predict the epidemic global effects. To address this issue, we present an Artificial Intelligence (AI)‐based meta‐analysis to predict the trend of epidemic Covid‐19 over the world. The powerful machine learning algorithms namely Naïve Bayes, Support Vector Machine (SVM) and Linear Regression were applied on real time‐series dataset, which holds the global record of confirmed, recovered, deaths and active cases of Covid‐19 outbreak. Statistical analysis has also been conducted to present various facts regarding Covid‐19 observed symptoms, a list of Top‐20 Coronavirus affected countries and a number of coactive cases over the world. Among the three machine learning techniques investigated, Naïve Bayes produced promising results to predict Covid‐19 future trends with less Mean Absolute Error (MAE) and Mean Squared Error (MSE). The less value of MAE and MSE strongly represent the effectiveness of the Naïve Bayes regression technique. Although, the global footprint of this pandemic is still uncertain. This study demonstrates the various trends and future growth of the global pandemic for a proactive response from the citizens and governments of countries. This paper sets the initial benchmark to demonstrate the capability of machine learning for outbreak prediction.

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Due to enlargement of social network and online marketing websites. The Blogs and reviews of the user are acquired from these websites. And these become useful for analysis and Decision making for various types of products, marketing and movie etc. with the extent of the usefulness of social Reviews. It is to be needed carefully analysis of that data. There are various techniques and methods are available that can accurately analyses the social information and provides greater accuracy for the analysis. But one of the major issues available with the social media data is that data is unstructured and noisy. It is to be required to solve this problem. So here in this paper a framework is proposed that includes latest data preprocessing techniques instead of noise removal like stemming, Lemmatization and Tokenization. After Pre-Processing of data ensemble methods is applied that increase the accuracy of previous classification algorithms. This method is inherent from bagging concept. First apply Decision Tree, Kneighbor and Naive Bayes classifier that not provide batter accuracy after that boosting concept is applied with the help of AdaBoost method that improves the accuracy of previous classical classifiers. At last our proposed ensemble method ExtraTree classifier is applied that inherent from bagging concept. Here we use the Extra Tree classifier that take the various sample are taken from training set and various random trees are created. It is also called as extremely randomized tree that provides extreme refined view. So that, it is to be conveying that The ExtraTree classifier of bagging ensemble method outperforms than all other techniques that are previously applied in this paper. with using some novel preprocessing techniques data that produced is more refined and that provides clean and pure base for the implementation of ensemble techniques. And also contributes in improving the accuracy of the applied methods.