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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.

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It is need to provide electricity for household use to each family in each locality including remote and trible belt of each state. This paper present a analysis of power supply using Solar and Wind hybrid energy in south-west of Rajasthan. This area includes parts of Dungarpur, Baswara, Udaipur, Sirohi, Pali, Jalore, Barmer and Jaisalmer. There are some Hilly/trible belts in south -west Rajasthan where the density of population is less than 100 persons per Sq. kilometer. Geographically this is hilly area where people live in scattered huts in mountainry area where it is very costely to supply electricity to each and every huts. To enlighten these huts in dark nights and to provide them electricity for household use in each and every session is the goal. Hybrid wind -solar Energy may be a boon for this area.Index Terms-Green energy, wind power; solar power; wind and solar hybrid power, Remote and hilly area.