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This paper is a study about COVID-19 cases in India to analyze and visualize the spread of COVID-19 cases in INDIA. For the analysis, the concept Exploratory Data Analysis (EDA) and some well know prediction models like Prophet Model, ARIMA Light GBM, Random Forest Regressor, XGBoost Regressor have been used to get the correct analyzed result. Furthermore, it has been observed that these prediction models and tools are helpful to analyze and visualize the COVID-19 situation in India and reason to spread it in optimal ways. Here, Matplotlib library has also been used to show the proper output in the form of graphs and charts. Analysis has been done on different Age/Gender Group. The Spike of Cases in India, State-wise Insights, the reason for the spread of COVID 19 cases. So, the main objective of this paper is to do analysis to know the reasonforCOVID-19 cases in India, State-wise Insight and State-Wise Testing, Prediction. To understand the optimal result of growth factors, the concept of Prophet Model ARIMA, LightGBM, Random Forest Regressor, XGBoost Regressor have been used on the data set. In addition, the data set includes the parameters of Age Group, Spike of Cases and Testing in India. From the literature survey, it has been observed that authors have used two and three techniques to analyze the result, but they have not used the methods we stated earlier for optimal result. This paper will provide the State-wise Insight and State-Wise Testing going in India with reason for spreading of COVID-19 cases. [ABSTRACT FROM AUTHOR]

Importance of analysis on datasets is very impactful in case of when raw datasets are needed to analyze for valuable insight. In this project three different datasets are chosen to analyze. As the main aim to find out which one is acceptable methodology in this analysis. For this analysis purpose various regression and soft computing methods have been applied. The main datasets which are needed to analyze are (i). Indian Population dataset. (ii). Sagar Island Tidal dataset, and (iii) West Bengal crop production dataset. All those datasets are collected from genuine sources. These datasets are needed to analysis so that the understanding of its insights can be finding out. Taking these datasets firstly preprocesses these to get the valuable information which are the informative data. The next step is analysis purpose, these all dataset are analyzed one by one to get finalized result. That means the methodologies like regression, deep learning technique have been applied to get which one is the best fit methodology on these datasets. So, the linear regression, nonlinear regression like linear, exponential, curvilinear and logarithmic equations have been used to find out corresponding errors likes root mean square error and percentage error. These errors demonstrate about the best fit methodology on the datasets. Beside regression technique also neural network has been applied. It has been found that neural network has given optimal less error than other technique when neural network has been applied. So, how all analysis have been done in consecutive way it is very important matter to observe. List of all datasets and methodologies which have been applied on these datasets are more discussed in the ‗implementation on datasets section'. Then result and conclusion section are to discuss about result and conclusion. Lastly some references are briefly detailed on the analysis topic. All the information about this analysis are seemed to be that the analysis on the datasets are a process of data analysis or data science. Using this data analysis technique it is understand that the valuable insight can be find out easily and necessarily [ABSTRACT FROM AUTHOR]