Classification and Prediction of Diabetics using Weka and Hive Tool

Abstract

Data mining is one such field which tries to extract some interesting facts from huge data set. Since many
years ago, the scientific community is concerned about how to increase the accuracy of different classification methods,
and major achievements have been made so far. Besides this issue, the increasing amount of data that is being generated
every day by various data logging methods raises more challenges. The objective of this project is to train historical
diabetes data and classifies them. Classifiers like Naïve Bayes, Decision Tree, Decision Stump, K star and Random forest
algorithms are trained very efficiently in a supervised learning setting. Though the chosen dataset is small in size, to
learn the big data tools and to find the effectiveness of the tools on a small data set Hadoop and Hive is used in this
paper.