• Kasarapu Ramani

Because of the extensive number of impacting factors, it is hard to foresee the natural disasters such as earthquake. Analysts are working seriously on earthquake forecast. Death toll and property can be limited with earthquake prediction. In this paper, the performance of the methods such as the Decision Tree and Naïve Bayes has been compared to find the algorithm that best fits the earthquake prediction. Also the performance of these classification algorithms is tested on two different tools such as Weka and Spark. The performance is expressed in terms of parameters correctly classified instances, incorrectly classified instances, errorrate and precision. The Decision Tree algorithm has given improved precision than the Naïve Bayes by approximately 3-4 percentage. It can be concluded by the analysis that the performance of both the algorithms was high when performed in the Spark tool as compared to the Weka tool.

Classification, Decision Tree, Naïve Bayes, Earthquake Prediction

"Performance of Classification Algorithms with Weka and Spark Tools", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 1, page no.629-631, January-2018, Available :http://www.jetir.org/papers/JETIR1801119.pdf

"Performance of Classification Algorithms with Weka and Spark Tools", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 1, page no. pp629-631, January-2018, Available at : http://www.jetir.org/papers/JETIR1801119.pdf