Location-Wise House Prediction Using Data Science Techniques
Subhani Shaik
Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana-501301, India.
V. Kakulapati
*
Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana-501301, India.
Ramakanth Reddy Malladi
Samaskruti College of Engineering, Ghatkesar, Hyderabad, Telangana, India.
*Author to whom correspondence should be addressed.
Abstract
This paper mainly deals with the prediction of location based on customer requirements. It also describes the location of the house, stores nearby by the house, duration of the house, transaction of data by the house and the latitude and longitude of the house, nearby station, and areas nearby house by using its contents to summarize the data. Take these contents on the x-axis and count on the x-axis then predict the data by using some algorithms in data science. Now, find the accuracy of data by training and testing, and then locate the particular location by using maps. Maps are the ones that can easily locate any region. By this, the customer can easily get access and can get the house with any legal requirements. The real-estate person can be gone through illegal things. In South India, propose to develop a model that can anticipate housing prices. It is an application of data science that makes use of algorithms. Prices of homes go up and down every day, and they aren't always proportional to the properties' actual values. Predicting the pricing of homes using only actual criteria is the primary emphasis of this effort. In this study, plan to conduct an analysis based on each of the fundamental criteria, and customer requirement are considered in the process of determining the prices.
Keywords: Data science, deep neural network, location of house, data prediction
How to Cite
Downloads
References
Kaggle competition. Available:https://www.kaggle.com/c/house-prices-advanced-regression-techniques.
Mckinsey report. Available:https://www.mckinsey.com/industries/capital-projects-and-infrastructure/ our-insights/getting-ahead-of-the-market-how-big-data-is-transforming-real-estate
Zillow Zestimate. Available:https://www.zillow.com/blog/zestimate-updates-230614/.
Kam C Chan, Patric H Hendershott, and Anthony B Sanders, Risk and return on real estate: evidence from equity reits, Real Estate Economics. 1990;18(4):431–452.
T. Chen and C. Guestrin, Xgboost: A scalable tree boosting system, In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016;785–794.
B. de Ville, Decision trees, wiley interdisciplinary reviews: Computational Statistics. 2013;5(6):448–455.
Dietterich TG. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization, Machine learning. 2000;40(2):139–157.
Fisher RA. Statistical methods for research workers, In Breakthroughs in statistics. 1992);66–70.
Franz Fuerst, George Matysiak. Analysing the performance of nonlisted real estate funds: a panel data analysis, Applied Economics. 2013;45(14):1777–1788.
Richard J Herring, Susan M Wachter. Real estate booms and banking busts: An international perspective, The Wharton School Research Paper. 1999;99-27.