Housing Price Prediction with Machine Learning

-22b9b34417bc6092a74331e 20220203060251182 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 01 30 2022 11 3 Housing Price Prediction with Machine Learning Department of ICT, Comilla University, Cumilla , Bangladesh. Amena Begum Nishad Jahan Kheya Department of ICT, Comilla University, Cumilla, Bangladesh. Md. Zahidur Rahman Department of CSE, Britannia University, Cumilla, Bangladesh. For socioeconomic development and the well-being of citizens, developing a precise model for predicting housing prices is always required. So that, a real estate broker or a house seller/buyer can get an intuition in making well-knowledgeable decisions from the model. In this work, a various set of machine learning algorithms such as Linear Regression, Decision Tree, Random Forest are being implemented to predict the housing prices using available datasets. The housing datasets of 506 samples and 13 feature variables from January 2015 to November 2019 were taken from the StatLib library which is maintained at Carnegie Mellon University. Since housing price is emphatically connected to different factors like location, area, the number of rooms; it requires all of this information to predict individual housing prices. This paper will apply both traditional and advanced machine learning approaches to investigate the difference among several advanced models to explore various impacts of features on prediction methods. This paper will also provide an optimistic result for housing price prediction by comprehensively validating multiple techniques in model execution on regression. 01 30 2022 42 46 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C9741.0111322 https://www.ijitee.org/portfolio-item/c97410111322/ Analysis of Air Traffic Management Models M.Tech, Department of Computer Science and Engineering, Rashtreeya Vidyalaya College of Engineering, Bengaluru, (Karnataka), India. Shankaramma Supreetha H V M.Tech, Department of Computer Science and Engineering, Rashtreeya Vidyalaya College of Engineering, Bengaluru, (Karnataka), India. Prof. Nagaraj G. S Associate Dean, Department of Computer Science and Engineering, Rashtreeya Vidyalaya College of Engineering, Bengaluru, (Karnataka), India. The high growth of air traffic flow has increased more bottleneck traffic issues in the air traffic management (ATM) system. The challenges between flight flow, air traffic control service and airspace are the major key parameters which support capability of domestic and international air transportation need to be looked by stakeholders. Many models are designed to incorporate to address the potential bottleneck issues of ATM. However, in these models’ analysis was not clearly presented. The proposed research review paper presents an analysis and insights of different models used in an air traffic management which includes, Big Data, Artificial Neural Network, Cloud Computing and Enterprise models. 01 30 2022 47 50 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C9752.0111322 https://www.ijitee.org/portfolio-item/c97520211322/