Agricultural crop production depends on various factors such as biology, climate, economy and geography. Several factors have different impacts on agriculture, which can be quantified using appropriate statistical methodologies. Applying such methodologies and techniques on historical yield of crops, it is possible to obtain information or knowledge which can be helpful to farmers and government organizations for making better decisions and policies which lead to increased production. In this paper, our focus is on application of data mining techniques to extract knowledge from the agricultural data to estimate crop yield for major cereal crops in major districts of Bangladesh.

Students need to have an effective education to take advantage of all the latest tools available. Even with a proper education, they are failing to reap its benefits; reasons involve social, economic and psychological factors a student faces during their adolescence. Our research is directed towards this particular problem of educational effectiveness. We have surveyed a large number of students across different districts in Bangladesh. Pre-processing was done thoroughly; the use of data balancing, dimensionality reduction, discretization and normalization in combinations has allowed us to derive the best model that could predict the academic performance based on different factors during the adolescence.

No abstract

No abstract

No abstract