IJSRST1841198 | Received : 25 Jan 2018 | Accepted : 08 Feb 2018 | January-February-2018 [ (4) 2: 829-831] © 2018 IJSRST | Volume 4 | Issue 2 | Print ISSN: 2395-6011 | Online ISSN: 2395-602X Themed Section: Science and Technology 829 Decision Tree Analysis Tool with the Design Approach of Probability Density Function towards Uncertain Data Classification Siripuri Kiran Assistant Professor Kakatiya Institute Of Technology and Sciences. Warangal, Telangana, India ABSTRACT Traditional decision tree classifiers are built utilizing certain or point data as it were. Be that as it may, in numerous genuine applications innately data is constantly uncertain. Quality or esteem uncertainty is characteristically connected with data esteems amid data gathering process. Traits in the preparation data sets are of two kinds – numerical (constant) and clear cut (discrete) characteristics. Data uncertainty exists in both numerical and all out characteristics. Data uncertainty in numerical qualities implies scope of qualities and data uncertainty in all out traits implies set or accumulation of qualities. In this paper we propose a technique for taking care of data uncertainty in numerical properties. One of the least difficult and most straightforward techniques for taking care of data uncertainty in numerical properties is finding the mean or normal or agent estimation of the arrangement of unique estimations of each estimation of a characteristic. With data uncertainty the estimation of a property is generally spoken to by an arrangement of qualities. Decision tree classification precision is tremendously enhanced when property estimations are spoken to by sets of esteems as opposed to one single delegate esteem. Probability density function with equal probabilities is one compelling data uncertainty demonstrating system to speak to each estimation of a property as an arrangement of qualities. Here the principle presumption is that genuine esteems gave in the preparation data sets are found the middle value of or delegate estimations of initially gathered esteems through data accumulation process. For every illustrative estimation of each numerical characteristic in the preparation data set, approximated values relating to the initially gathered esteems are created by utilizing probability density function with equal probabilities and these recently produced sets of qualities are utilized as a part of developing another decision tree classifier. Keywords : Probability Density Function, Data Mining, Classification, Uncertain Data, Decision Tree, Machine Learning. I. INTRODUCTION Classification is a data investigation method. Decision tree is a capable and well known instrument for classification and forecast yet decision trees are predominantly utilized for classification [1]. Primary favorable position of decision tree is its interpretability – the decision tree can without much of a stretch be changed over to an arrangement of IF-THEN decides that are effortlessly justifiable [2]. Cases wellsprings of data uncertainty incorporate estimation/quantization blunders, data staleness, and various rehashed estimations [3]. Data mining applications for uncertain data are – classification of uncertain data, bunching of uncertain data, visit design mining, exception discovery and so forth. Cases for specific data are – areas of colleges, structures, schools, universities, eateries, railroad stations and transport stands and so on. Data uncertainty normally emerges in an extensive