International Organization of Research & Development 2020-08-15T13:07:03+00:00 A. S. Manikrao Open Journal Systems <p id="just"><em>Welcome to the website of the<strong> International Organization of Research &amp; Development</strong></em> - IORD is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education. IORD covers all the fields of Science, Engineering &amp; Management. IORD is published by Knowledge Consortium Publication (KCP), Mumbai, India. Recognized internationally as the leading peer-reviewed interdisciplinary journal devoted to the publication of original papers, it serves as a forum for practical approaches to improving quality in issues pertaining to Science, Engineering and Management.</p> <center> <p><img src="" alt="IORD Indexing" width="700" height="350" /></p> </center> <p id="just">IORD invites original research, review papers, survey papers, short communication, case study reports, monographs and technical notes. Submit paper by using register tab present in top right corner of this website or you can mail your article to <a href=""></a></p> <p id="just">The publisher is always open to constructive feedback. We pride ourselves on our commitment to serving the Open Access community and work hard to become better at what we do. We invite your concerns, questions, and complaints. Contact us at <a href=""></a>. We will get back to you in 24-48 hours. You may also call or Whatsapp on +91 8888605777.</p> <p><strong>Submissions are Invited for in the field of:</strong></p> <p><strong>*Engineering and Technology</strong></p> <p><strong>* Science and Humanities</strong></p> <p><strong>* Management</strong></p> Determining House Price Using Regression 2020-08-15T13:07:03+00:00 Anirban Chakraborty <p>The purpose of this article is to estimate the purchasing and sale opportunities of houses on the market by Machine<br>learning techniques. For financial stability, the housing sector is quite critical. People seeking to purchase a new house appear to<br>be more cautious in their expectations and sales tactics analyzing historical industry patterns and pricing levels, as well as<br>potential changes. The index of our method consists of the price of the house and its efficiency metrics, such as the amount of<br>renovation, the distance from the city center, the construction programs, the height of the property, the floor and the location of the<br>apartment in the home, and there are several other criteria. Service includes a database that recognizes the preferences of its<br>clients and then integrates machine learning software. The program will enable consumers invest in real estate without<br>approaching brokers. It, therefore, reduces the uncertainties inherent with the deal. The program has a login ID and a pin. At the<br>same time, when the user searches for an attribute, the value of the original attribute and the value of the predicted attribute are<br>displayed.</p> 2020-08-01T00:00:00+00:00 Copyright (c) 2020