Electronic Governance of Housing Price using Boston Dataset Implementing through Deep Learning Mechanism
E. Laxmi Lydia1, Gogineni Hima Bindu2, Aswadhati Sirisham3, Pasam Prudhvi Kiran4
1E. Laxmi Lydia, Department of Computer Science Engineering, Vignan’s Institute of Information Technology Autonomous, Visakhapatnam (Andhra Pradesh), India.
2Gogineni Hima Bindu, Assistant Professor, MCA, Vignan’s Institute of Information Technology Autonomous, Visakhapatnam (Andhra Pradesh), India.
3Aswadhati Sirisha, Assistant Professor, MCA, Vignan’s Institute of Information Technology Autonomous, Visakhapatnam (Andhra Pradesh), India.
4Pasam Prudhvi Kiran, Assistant Professor, IT, Vignan’s Institute of Information Technology Autonomous, Visakhapatnam (Andhra Pradesh), India.
Manuscript received on 26 March 2019 | Revised Manuscript received on 05 April 2019 | Manuscript Published on 27 April 2019 | PP: 560-563 | Volume-7 Issue-6S2 April 2019 | Retrieval Number: F10750476S219/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The growth of technology in our day-to-day enterprise with advanced machines are outstanding through Artificial Intelligence involving both machine learning and deep learning all over the world. As things go on the forecast of innovations to business and society applying Artificial Intelligence influence technological transformations. This will possibly lead to vulnerable with reference to security. In this paper, we intend to constitute particular prediction forms depending on deep learning to regulate the actual data of the real estate processed apartments data in Boston to predict the housing price. We construct a Linear regression prediction model related to Supervised Learning in Artificial Intelligence. In this paper, a comprehensive study on house pricing using different class labels. Finally, the supervised data was produced, which is important to estimate and prediction of the housing price in the real estate business. Connecting with Artificial Intelligence, we will acquire the capacity of composing higher intelligent predictions regarding future management and developments on smarter intelligent systems and prototypes.
Keywords: Artificial Intelligence, Supervised Learning, Smarter Intelligent, Deep Learning.
Scope of the Article: Deep Learning