ISSN: 2320-5407 Int. J. Adv. Res. 4(11), 1351-1355 1351 Journal Homepage: -www.journalijar.com Article DOI:10.21474/IJAR01/2219 DOI URL: http://dx.doi.org/10.21474/IJAR01/2219 RESEARCH ARTICLE A SURVEY ON FEATURE SELECTION FOR EFFICIENT ECONOMIC BIG DATA ANALYTICS. Ms. DeepthiMogaparthi, Prof. Priyadarshani Kalokhe, Ms. Punam Patil, Ms. Pooja Shedutkar, Ms. Sharda Tenginkai. Computer Department Alard College Of Engineering & Management, Pune. …………………………………………………………………………………………………….... Manuscript Info Abstract ……………………. ……………………………………………………………… Manuscript History Received: 28 September 2016 Final Accepted: 30 October 2016 Published: November 2016 Key words:- Feature selection, big data, clustering, economy, urbanization. Huge amount of data gets collected every day and there is also a need of technology to handle enormous amount of economic data. Hence there are various and huge number of opportunities for economic analysis.Lowquality, high-dimensionality and great volume pose great challenges on efficient analysis of economic big data. To overcome these challenges our paper presents a new structure for efficient analysis of high-dimensional economic big data based on innovative distributed feature selection. The presented framework combines the methods of economic feature selection and econometric model construction to discover the hidden patterns for economic development. General Terms:- Economic Big Data Analysis Copy Right, IJAR, 2016,. All rights reserved. …………………………………………………………………………………………………….... Introduction:- Big data is considered as very significant in solving social and e-commercial issues. [1] Every day 2.5 Quintillion bytes is being produced every day. Viewing all such issues big data offered a stupendous opportunity for the energy efficiency economy, and national security. Having just enormous amount of data is insufficient, because our interests are focused on the “prized” information. When consumers purchase products through online, products information such as ratings, product reviews, product descriptions given by sellers are very useful for consumers to optimize their purchasing decisions. However, when a consumer purchases used products via online e-commerce sites, the consumer may consider much more attributes about the products than that for purchasing new products. This is due to the need for understanding instance-specific conditions before purchasing a used product and thus the available descriptions for a used product may differ in each other. [20] Big Data analytics requires business processes to change and it must align with the organization’s IT infrastructure to support the business initiatives. New ways of doing data analytics and business intelligence impact on technology infrastructure components. Here we explore the hidden relations between economy and its response indicators from a new angle and extract the meaningful knowledge from economic big data in order to derive right insights and conclusions on an innovative distributed feature selection that integrates advanced feature selection techniques and econometric methods. Corresponding Author:-Ms. Deepthi Mogaparthi. Address:-Computer Department Alard College Of Engineering & Management, Pune.