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An Analysis of Teachers’ Performance using Decision Tree Based C5.0 Mapreduce Algorithm using Bigdata Mining

V. Shanmugarajeshwari, R. Lawrance


Data mining is one of the potential research fields regarding interdisciplinary aspects. Educational data mining is developing discipline in the present scenario. Classification techniques in the data mining plays an important role in the area of educational data mining. The main goal regarding this work is to predict the teachers’ performance by using the relevant features. The proposed methodology consists of the phases like preprocessing, attribute selection, classification based on decision tree and performance evaluation. In the data preprocessing phase, the missing values have been filluped. The attributes are converted into a categorized format using the categorization MapReduce process. The gain ratio with MapReduce is the best method for feature selection, since it selects technique extracted the relevant attributes in an accurate manner, which takes less time compares to the other feature selection methods. This paper presents a MapReduce algorithm on the classification structure by using C5.0 algorithm, aiming to accumulate time and obtain high accuracy on huge students’ and teachers’ datasets. The classification process based on C5.0 MapReduce algorithm is resulted with good classification accuracy.


Educational Data Mining, Students’ and Teachers’ dataset, MapReduce, Classification, C5.0 Algorithm, Big Data Mining.

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