PREDICTION OF POPULAR SMARTPHONE COMPANIES IN THE SOCIETY

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1 PREDICTION OF POPULAR SMARTPHONE COMPANIES IN THE SOCIETY T.Ramya 1, A.Mithra 2, J.Sathiya 3, T.Abirami 4 1 Assistant Professor, 2,3,4 Nadar Saraswathi college of Arts and Science, Theni, Tamil Nadu (India) ABSTRACT This Paper Mainly focus on the impact of Smartphone on business, education.the ability to predict the peoples selection for Smartphone s on business persons, employees and home maker is to identify their opinion on what characteristic they are selecting the Smartphone which Smartphone s they are willing to choose and based upon which company are find out for that questionnaire has been designed and circulated among different kinds of peoples around two district and the collected dataset are tested using Rapid Miner 6.0 by implementing ID3 algorithm. Keywords: ID3, Rapid Miner, Smart Phones. I. Introduction 1.1 Data Mining Data mining is an analytic process designed to explore data in search of consistent pattern and/or systematic relationships between variables, and then to validate the finding by applying the detected pattern. Data mining is the process of analyzing data from different perspective and summarizing it into useful information-information that can be used to increase revenue. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. 1.2 Id3 Algorithm ID3 (Iterative Dichotomiser 3) is an algorithm used to generate a decision tree invented by Ross Quinlan. ID3 is the precursor to the C4.5 algorithm. Very simply, ID3 builds a decision tree from a fixed set of examples. The resulting tree is used to classify future samples. The examples of the given ExampleSet have several attributes and every example belongs to a class (like yes or no). The leaf nodes of the decision tree contain the class name whereas a non-leaf node is a decision node. The decision node is an attribute test with each branch (to another decision tree) being a possible value of the attribute. ID3 uses feature selection heuristic to help it decide which attribute goes into a decision node. The required heuristic can be selected by the criterion parameter. The ID3 algorithm can be summarized as follows: Take all unused attributes and calculate their selection criterion (e.g. information gain) Choose the attribute for which the selection criterion has the best value (e.g. minimum entropy or maximum information gain) Make node containing that attribute 100 P a g e

2 ID3 searches through the attributes of the training instances and extracts the attribute that best separates the given examples. If the attribute perfectly classifies the training sets then ID3 stops; otherwise it recursively operates on the n (where n = number of possible values of an attribute) partitioned subsets to get their best attribute. The algorithm uses a greedy search, meaning it picks the best attribute and never looks back to reconsider earlier choices. Some major benefits of ID3 are: Understandable prediction rules are created from the training data. Builds a short tree in relatively small time. It only needs to test enough attributes until all data is classified. Finding leaf nodes enables test data to be pruned, reducing the number of tests. 1.3 Mobile Application s Effect in Society from the Ethical Perspective Not only individuals or business, the mobile application also has a great effect in society. The whole society can be facilitate using mobile application. Some issues of social effect describe bellow. Quick communication Save time and increase productivity Increase Job vacancy Less computer use less power consumption Considerable Cost Saving Entertainment 1.4 Limitation of Mobile Application One of the big challenges of mobile application is its platform capability and limitation. Beside the interesting usability of mobile application they have some more interesting platform problems and limitation. We are trying to discuss the limitation in below. 1. Small Screen Size: In mobile platform it is difficult or impossible to view text and graphics like a desktop computer screen. 2. Lack of windows: In desktop we can see many windows at a time. But in mobile platform it is difficult. 3. Navigation: Most mobile devices do not have mouse like pointer, so it has limited flexibility in navigation. 4. Types of pages accessible: The mobile platform does not support all type of file format. 5. Speed: The speed of processing and speed of connectivity of mobile platform is slow. II. METHODOLOGY 2.1 Objective The main objective of the Prediction of popular Smartphone companies in the society is to use data mining methodologies and to predict peoples/citizen selection of Smartphone s. In this research, the classification task is used To Determine the peoples/citizen choice of smartphone characteristic. 101 P a g e

3 2.2 Data Collection and Data Set Preparation For the Prediction of Popular Smart Phone Company on the Society, Questionnaire has been designed and circulated among 137 various kinds of peoples around Theni and Dindigul district in Tamil Nadu. Finally the Collected data sets are entered in Ms-Excel and imported at Rapid Miner6.0 and ID3 Algorithm is implemented. Table 1.0: data Structure of the basic data. S.NO ATTRIBUTE PEOPLES 1. Business 46 Persons 2. Employees Housewives Rapid Miner Rapid Miner is an open source data mining tool that provides data mining and machine learning procedures including data loading and transformation, data preprocessing and visualization, Modelling, evaluation, and deployment. It is written in the Java programming language. We have used Rapid Miner to generate decision trees of ID3 algorithms. 2.4 Decision Tree In decision tree learning, ID3 (Iterative Dichotomiser 3) is an algorithm invented by Ross Quinlan used to generate a decision tree from the dataset. ID3 is typically used in the machine learning and natural language processing domains. The decision tree technique involves constructing a tree to model the classification process. Once a tree is built, it is applied to each tuple in the database and results in classification for that tuple. The following issues are faced by most decision tree algorithms: Choosing splitting attributes Ordering of splitting attributes Number of splits to take Balance of tree structure and pruning Stopping criteria 2.5 Classification and Prediction There are two forms of data analysis that can be used for extract models describing important classes or predict future data trends. These two forms are as follows: Classification Prediction These data analysis help us to provide a better understanding of large data. Classification predicts categorical and predictions models predicts continuous valued function. 102 P a g e

4 III. RESULT AND DISCUSSION The Original Dataset is applied in Rapid Miner tool to produce the following Result. In this survey the number of business peoples are 46, employees are 46 and housewives are 45 the graphical representation of various kinds of peoples details are displayed below using Rapid Miner Tool by using Histogram Plot View. Figure 1.1 Usage of Smartphones In Society The Most Popular Smart Phone Companies are Predicted for that dataset are implemented using Rapid Miner Tool and the result are show as pie chart according to the result most people in the society choose Samsung SmartPhones when compared with others FIGURE 1.2 Popular SmartPhone Company 103 P a g e

5 IV. CONLUSION The Prediction of Popular Smart Phone Company in the Society are identified and the impact of Smart Phone towards society are predicted Finally the result was generated by using various graph and decision tree using Rapid Miner Tool. REFERENCES [1]. Social Data Mining On Smartphones;Ajita Gupta; International Journal Of Computer Science, Engineering And Information Technology (Ijcseit ) Vol,5, Issue 3, July 7, 2011 [2]. Smartphone app usage log mining ;woan-rou tseng and kuo-wei hsu, international journal of computer and electrical engineering, vol. 6, issue 2, [3]. Mining personal data using smartphones and earable devices:a survey ; muhammad habib urrehman, chee sun liew, teh ying wah, junaidshuja and babakdaghighi,journal of faculty of computer science and information technology,vol.3, issue. 4,13 feb2015 [4]. Mobile Marketing Association, 2008, USA 1670 Broadway, Suite 850, Denver, CO [5]. T. Hsu, "Real-time risk control system for CNP (card not present)", 17th ACM SIGKDD [6]. international conference on Knowledge discovery and data mining, pp , [7]. IBM Survey: IT Professionals Predict Mobile and Cloud Technologies Will Dominate Enterprise Computing By 2015, Posted October 17, 2010, URL: [8]. Markus Hofmann, Ralf Klinkenberg, RapidMiner: Data Mining Use Cases and Business Analytics Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series), CRC Press, October 25, [9]. [10]. [11]. Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems), Jiawei Han, Morgan Kaufmann, [12]. Data Mining Techniques: For Marketing, Sales, and Customer Support, Michael J. A. Berry, Gordon S. Linoff, Wiley, P a g e