Data Analysis on Student's Performance based on Health status using Genetic Algorithm and Clustering algorithms

Data analysis is the emerging research field that relies on methods and techniques to make insights on the data sets. Data analysis on student's academic Performance based on their Health status such as nutritious food intake, hygienic life style and frequency of health issues is the main objective of the research. The datasets were obtained by Questionnaire method and the analysis were carried out initially with clustering algorithms such as K-means algorithm, Hierarchical clustering and EM Method. In the second phase, Genetic search was performed and the outputs were generated. The statistical output representation for the important attributes are given using orange software. The algorithmic Experimental setup was also carried out with weka datamining tool on student's dataset that has 113 instances and 93 attributes. The findings of the research work were that K-means algorithm outperformed well when compared with EM method and Hierarchical clustering. Genetic search method predicted correlated attributes for the selected class attribute and the outputs are generated. The statistical data analysis shows that nutrition and health issues of female students has an impact on the academic performance of students.