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Using Factor Classification for the Slow Learner Prediction over Various Class of Student Dataset
Objective: The slow learner prediction is the branch of the automatic predictive method for the students learning abilities. The high school student data has been obtained from the schools from the diverse regions in Punjab, a pivotal state of India. The selective data of almost 2400 students approximate obtained from different school in the regions has been undergone the test using the proposed model in this paper. Method: The analysis can be entirely performed over the student performance data. The proposed model is established upon the naïve bayes classification model for the data classification and predicts the dataset. Using the multi-factor features obtained from the input dataset. The classification algorithm has been applied individually over data grouped in the various groups of subjects. Finding: In this model four factor are used for predict the student performance. Two factor result are almost same but the another two factor result is different from two previous factor. The subject groups have been divided into the difficult and normal group the proposed system will be capable of performing the deep analysis over the student data obtained from high-school, the proposed model results have been show in that the deep analysis of the data tells the in-depth facts from the input data. Improvement: The effective accurate classification model is considered in this proposed model, when evaluated from the results described in the earlier sections.
EDM, Factor Classification, NBC, Web Mining.
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