In the modern era agriculture development is the highly contribute field of food security. Data Science is one of the top analysis experimental methods for forecasting and mapping synchronize. In our study, we experiment with three major parameters (Rainfall, Relative Humidity and Temperature) that can be affected crop production rate as well as area-based mapping. To complete the procedure, the cluster groping and prediction system has created a machine learning BOT combined analysis system. Bangladesh and its 13 areas with 46 years of data have visualized with proper analysis and build up a 2D map of each separate production area. Multi Linear Regression (MLR) and KMean Clustering is the main key point algorithm for the production analysis. Experiment analyzing, we can see that some elements of our environment are closely associated with the productivity of the crop. An untactful environmental change on parameters (Rainfall, Humidity, and Temperature) reduces agricultural productivity by 32-38%. Developed model accuracy 91.25% forecasting methodological analysis for production mapping and prediction. Extreme population food security has ensured ICT and Agriculture combine BOT & EVPM method is essential for the scientific world. This study will allow farmers to choose the proper crop in the right environmental condition, which will play a key role in strengthening the economy of the country.

In the present situation, COVID-19 is a very common and dangerous issue in the whole world. Ensuring our healthy mental state is very essential at the period of COVID-19. But as a result of being in the home quarantine for a long time, people are going to notice a mental change such as stress, depression, mood swing. We proposed an RHMCD model which helps us to reach our required goal. This model contains machine learning algorithms. We examined our work with Naive Bayes classifiers, Support Vector Machine, and logistic regression. For gaining the report of mental conditions we used the sentiment analysis technique. For measuring the level of depression we also used a decision tree approach.