International Journal of Computer Science Trends and Technology (IJCST) – Volume 7 Issue 3, May - Jun 2019 ISSN: 2347-8578 www.ijcstjournal.org Page 141 Prediction of the Data Analysis Using Decision Tree P.T.V.Lakshmi, B.Nivegeetha Assistant Professor NadarSaraswathi College of Arts and Science,Theni India ABSTRACT Data mining is the process of discovering the patterns in large data set`s at the intersection of machine learning, statistics and database systems. Data mining is the analysis step of KDD. In this paper we are going to analyze the Iris data sets for sample and going to construct the decision tree based on their individual characteristics. The classification technique is used to accurately predict the target class for each case in the data. In this paper we are going to analyze the datasets using the powerful tool known as “Rapid miner”. Keywords:- Data mining, classification, decision tree, rapid miner. I. INTRODUCTION Data mining is a computer science term also known as Knowledge Discovery in Databases (KDD). It was about finding new information in a lot of data. The data is stored so that it can be used later. Data mining is primarily used by industries like retail, financial and marketing companies. If you have ever shopped at a retail store and received customized coupons that’s the result of mining (i.e.) your individual purchase history was analyzed to find out what products you have been buying and what promotions you are likely to be interested in. By analyzing the datasets the decision tree was built based on the classification techniques using the “Rapid miner tool”. Through this paper we are going to construct the decision tree for Iris datasets. RAPID MINER TOOL Rapid miner is a data science software platform developed by the company of the same name that provides an integrated environment for data preparation , machine learning, deep learning ,text mining and predictive analytics. It is used mainly for business and commercial applications. It is developed on a open core model. Its initial release was before 13 years. It was formally known as YALE (Yet Another Learning Environment). It uses the client/server model with the server offered as either on-premise, or in public or private cloud infrastructure. It is written in java programming language and it provides GUI to design and execute the analytical workflows. Those workflows are called “processes” and it consists of multiple operators. It provides learning schemes, models and algorithms using python scripts. About 50 developers participate in the development of the open source rapid miner. II. CLASSIFICATION Classification comes under the category of supervised learning (i.e.) the training data are accompanied by labels indicating the class of the observations. It is a data mining function that assigns items in a collection to target category. The main goal is to accurately predict the target class. The simplest type of classification problem is binary classification. Classification models are tested by comparing the predicted values to known target values in a set of test data. They are divided into two data sets 1. Building the model 2. Testing the model III. CLASSIFICATION ALGORITHMS • Linear classifiers • Support vector machines • Quadratic classifiers RESEARCH ARTICLE OPEN ACCESS