A classification algorithm is used to assign predefined classes to test instances for evaluation) or future instances to an application). This study presents a Classification model using decision tree for the purpose of analyzing water quality data from different counties in Kenya. The water quality is very important in ensuring citizens get to drink clean water. Application of decision tree as a data mining method to predict clean water based on the water quality parameters can ease the work of the laboratory technologist by predicting which water samples should proceed to the next step of analysis. The secondary data from Kenya Water institute was used for creation of this model. The data model was implemented in WEKA software. Classification using decision tree was applied to classify /predict the clean and not clean water. The analysis of water Alkalinity,pH level and conductivity can play a major role in assessing water quality. Five decision tree classifiers which are J48, LMT, Random forest, Hoeffding tree and Decision Stump were used to build the model and the accuracy compared. J48 decision tree had the highest accuracy of 94% with Decision Stump having the lowest accuracy of 83%.
Data mining means mining knowledge from large amount of data. Classification is one of the essential role in the field of healthcare. Diagnosis of health conditions is a very important and challenging task in field of medical science. There are various types of diseases are diagnosis in medical science. Heart disease is the leading cause of death in the world over the past ten years. Heart diseases classification is one of the important problems in medical science because it is directly related to health condition of human body, this type of disease can be solved by proper identify and carefully treatment. In this paper Attribute selection, Naive Bayes, Gini index and Bayesian classification are applied for heart disease data. And also compared for classifying the accuracy.