Abstract: Nowadays, sustainable development is considered a key concept and solution in creating a promising and prosperous future for human societies. Nevertheless, there are some predicted and unpredicted problems that epidemic diseases are real and complex problems. Hence, in this research work, a serious challenge in the sustainable development process was investigated using the classification of confirmed, death, and recovery cases of COVID-19 as one of the epidemic diseases. The inception of the coronavirus was the fish market of Wuhan city, Hubei territory in China. The instances of somebody experiencing COVID-19 can be followed back to the finish of December 2019 in China. This is the most irresistible malady and spread worldwide inside a quarter of a year after the main case announced. The complete name of the coronavirus is serious intense respiratory disorder SARS-CoV. Thus, the data mining predictive modelling method of data handling and predictive or forecasting the spread of COVID-19 virus. The full name of the coronavirus is severe acute respiratory syndrome SARS-CoV. It spread on humans as well as animals and infected around 213 countries and territories with 6,399,977 confirm cases and 378,065 deaths till 2 June 2020.  This study introduces the spreading pattern of COVID-19 in the top ten infected countries.  After China, European countries are the most infected ones. In this study, data was analysed on the attributes confirmed, active, recovered and death cases and the next 14 days outbreak prediction.  This research work mainly works on worldwide COVID 19 data analysis and forecasting by using fbprophet. Prophet it is a python library package used for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonally, plus holiday’s effect. Being the COVID-19 pandemic during a very short time span, it is very important to analyze the trend of these spread and infected cases. The real-time datasets are used in this project and it plotted on the worldwide map. Firstly analyse the no. of infected people around the world, the no. of people who died, and the no. of recovered people in the world on the basis of real-time data. Secondly predicting and visualizing the Number of COVID-cases in India using the Fbprophet algorithm.

Keywords: Analysis and Visualization, Time Series, Fbprophet, COVID-19

PDF | DOI: 10.17148/IJARCCE.2020.9532

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