This study examines the factors affecting students' academic performance that contribute to the prediction of their failure and dropout using educational data mining techniques. This paper suggests the use of various classification techniques to identify the weak students who are likely to perform poorly in their academics. WEKA, an open source data mining tool was used to evaluate the attributes predicting student failure. The data set is comprised of 67 attributes of 150 students who have enrolled in B. Tech Degree Course registered for the academic year 2014-18 in a reputed college in Kerala affiliated to M.G University, Kerala, India. Various classification techniques like induction rules and decision tree have been applied to the data. The results of each of these approaches have been compared to select the one that achieves high accuracy.
Diabetes the silent killer which kills part by part of our life Diabetes can strike anyone, from any walk of life. It is not only a disease but also a creator of different kinds of diseases like heart attack, blindness, kidney diseases etc. It is found that in last decades the cases of people living with diabetes jumped almost 350 million worldwide. Early detection of diabetes can reduce the health risk in patients. With rise of new technology like machine learning we have a solution to this issue, a system for predicting the chance of diabetes. Machine learning techniques increase medical diagnosis accuracy and reduce medical cost. This paper aims to predict diabetes via supervised machine learning algorithms such as decision tree. In our work we have used the Pima Indians Diabetes Dataset from UCI Machine Learning Repository.
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