click here to download project abstract of data mining
ABSTRACT
Most of the schemes introduced by government go into the dustbin just because the officials who implement the schemes could not make them available to suitable people. So there is a secured and transparent system needed which enable an arbitrary person to directly apply for a scheme and track the status from time to time and know whether he is entitled to receive the fruit or his application is rejected by officials. In our system admin will add the scheme details to the system. And he can able to the view the registered user details. And he can able to accept or reject the scheme which is requested by the client. And user can able to view the status of his scheme request level. It contains a small description of ML/DM which are used by the researchers. It also describes data sets as very important in ML/DM methods. Machine Learning becomes most popular in the field of IT industry. Nowadays Machine Learning and Data Mining turn as a powerful technique which applicable for various fields such as IT, Education sector and also in business sector too. The different types of ML/DM algorithms are addressed by using all this technique. The algorithms which give more accuracy results in detection of continuity of every student’s scholarship such as Naïve Bayes, Decision Tree and k-NN. Finally, the proposed model will provide a list of candidates, who deserve to have a scholarship and also discussion has been made on accuracy of each techniques which was used to get a result. This comprehensive review navigates through diverse data mining and machine learning techniques applied to predict student scholarship eligibility. Exploring algorithms like decision trees, neural networks, and ensemble methods, it evaluates their efficacy in analyzing academic, demographic, and financial data to forecast students’ likelihood of receiving scholarships.
