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Diabetes is one of the prevalent diseases all over the world. As per the International Diabetes Federation (IDF) report of the year 2017, diabetes is prevalent in about 8.8% of the Indian adult population and is one of the top ten causes of death in India. In untreated and unidentified diabetes could cause fluctuations in the sugar levels and extreme cases, damage organs such as kidneys, eyes, and arteries in the heart. By using Machine learning algorithms to predict the disease from the relevant datasets at an early stage could likely save human lives. The purpose of this investigation is to assess the classifiers that can predict the probability of disease in patients with the greatest precision and accuracy. Experimental work has been carried out using classification algorithms such as K Nearest Neighbor (KNN), Decision Tree(DT), Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest(RF) on Pima Indians Diabetes dataset using nine attributes which is available online on UCI Repository. The performance of classifier is evaluated based on precision, recall, accuracy and is estimated over correct and incorrect instances. The results proved that Logistic Regression (LR) performs better with the accuracy of 77.6 % in comparison to other algorithms

COVID-19 pandemic has affected the economy and changed the human way of life, disrupting everyone's mental, physical, and financial well-being. Many of the fastestgrowing economies are strained owing to the severity and communicability of the epidemic. Because of the increasing diversity of cases and the resulting burden on healthcare practitioners and the government, therefore, predicting the number of infected COVID-19 cases which could be useful in planning the required hospital resources in the future. In this paper, we focussed on information-led methods of estimating the numbers of COVID-19 confirmed cases in the country and their implications in the future, using different learning models such as Sigmoid modelling, ARIMA, SEIR model and LSTM, for protective measures, such as social isolation or the lockout of COVID-19. . Use of raw data by separating an event from the previous event in order to set the time series. The computation of number of positive incidents, number of rereferred incidents are reliable within a limited range. A datadriven forecasting method has been used to approximate the total confirmed cases in coming months. These LSTM model gave very promising results than other models. Hence, this work would help the decision makers to understand the upcoming of the pandemic trajectory in the country and take necessary actions for the effect of interventions.

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