RooMeet Rooms and Room Mate Finding Website

Abstract

Nowadays, roommate services are in high demand these days. With the world getting flattered by the day, people have transcended physical boundaries to look for study and work options in places they haven't visited before. Although a new place throws up its unique challenges, one is today well-equipped to deal with them. The advent of the net has made this possible, with people finding it increasingly easy to relocate to a new apartment and find a suitable roommate in no time.  RooMeet: Rooms and Room- Mate finding website is a website that makes sure you find good flatmates with the specific requirements the user has provided. The user will be asked to log in or create an account to enter the website. Then they will be asked a few questions about themselves and the roommate they prefer to be with. With the logistic regression algorithm and recommendation algorithm with collaborative filtering, they will get suggestions as to who can be the perfect fit for their flat buddy, and then if the other end user feels the same way they will enable the option of texting each other and can have a great conversation and see if they are a good fit or not. RooMeet: Rooms and Room- Mate finding website offers a marketplace where you can search for potential roommates. RooMeet: Rooms and Room- Mate finding website will use a highly advanced search algorithm to make the search much more effective. With comprehensive search options, this site makes sure that only results that meet your specific requirements are displayed.

Country : India

1 Rutika Sahane2 Saurabh Andhale3 Bidwe Sanket4 Potdar Prathamesh5 Prof. Rajendra Sabale

  1. Student, Computer Engineering, Sir Visvesvaraya Institute of Technology, Nashik, Maharashtra, India
  2. Student, Computer Engineering, Sir Visvesvaraya Institute of Technology, Nashik, Maharashtra, India
  3. Student, Computer Engineering, Sir Visvesvaraya Institute of Technology, Nashik, Maharashtra, India
  4. Student, Computer Engineering, Sir Visvesvaraya Institute of Technology, Nashik, Maharashtra, India
  5. Professor, Computer Engineering, Sir Visvesvaraya institute of technology, Nashik, Maharashtra, India

IRJIET, Volume 8, Issue 4, April 2024 pp. 170-176

doi.org/10.47001/IRJIET/2024.804023

References

  1. Sanidhya kuchhal1, Kushagra Saini1, Karan Sharma1, Anjali Kumari1," WEB BASED APPLICATION ON FLATMATE SEARCHING SYSTEM " Department of Computer Science, ABES Institute of Technology, Ghaziabad 201009Uttar Pradesh India, Volume: 09 Issue: 05 | May 2022.
  2. YUNPENG LI 1,2, YICHUAN JIANG 2 , (Senior Member, IEEE), WEIWEI WU 2 , JIUCHUAN JIANG 3 , AND HUI FAN4," Room Allocation With Capacity Diversity and Budget Constraints "", Received March 2, 2019, accepted March 19, 2019, date of publication March 27, 2019, date of current version April 13, 2019.
  3. Dr. Severin Klingler Sumit Kumar Ram Prof. Dr. Markus Gross Prof. Dr. Didier Sornette, "A streamlined and image-focused Platform to connect Flatmates", Ferdinand Wittmann Bachelor Thesis March 2020.
  4. S. Temür, M. Akgün, and G. Temür, “Predicting Housing Sales in Turkey Using Arima, Lstm and Hybrid Models,” J. Bus. Econ. Manag., vol. 20, no. 5, pp.920–938, 2019, doi: 10.3846/jbem.2019.10190.
  5. A.Ebekozien, A. R. Abdul-Aziz, and M. Jaafar, “Housing finance inaccessibility for low-income earners in Malaysia: Factors and solutions,” Habitat Int., vol.87, no. April, pp. 27–35, 2019, doi: 10.1016/j.habitatint.2019.03.009.
  6. A.Jafari and R. Akhavian, “Driving forces for the US residential housing price: a predictive analysis,” Built Environ. Proj. Asset Manag., vol. 9, no. 4, pp.515–529, 2019, doi: 10.1108/BEPAM-07-2018-0100.
  7. Choong Wei Cheng, “Statistical Analysis of Housing Prices in Petaling,” UniversitiTunku Abdul Rahman, 2018.
  8. R. E. Febrita, A. N. Alfiyatin, H. Taufiq, and W. F. Mahmudy, “Data-driven fuzzy rule extraction for housing price prediction in Malang, East Java,” 2017 Int.Conf. Adv. Comput. Sci. Inf. Syst. ICACSIS 2017, vol. 2018-Janua, pp. 351–358, 2018, doi: 10.1109/ICACSIS.2017.8355058.
  9. G. Gao et al., “Location-Centered House Price Prediction: A Multi-Task Learning Approach,” pp. 1–14, 2019, [Online]. Available:http://arxiv.org/abs/1901.01774.
  10. T. D. Phan, “Housing price prediction using machine learning algorithms: The case of Melbourne city, Australia,” Proc. - Int. Conf. Mach. Learn. Data Eng.iCMLDE 2018, pp. 8–13, 2019, doi: 10.1109/iCMLDE.2018.00017.
  11. 12.       Y. Y. S. Song, T. Zhou, H. Yachi, and S. Gao, “Forecasting house price index of China using dendritic neuron model,” PIC 2016 - Proc. 2016 IEEE Int. Conf. Prog. Informatics Comput., pp. 37–41, 2017, doi: 10.1109/PIC.2016.7949463.
  12. R. AswinRahadi, S. K. Wiryono, D. P. Koesrindartoto, and I. B. Syamwil, “Factors Affecting Housing Products Price in Jakarta Metropolitan Region,” Int. J.Prop. Sci., vol. 6, no. 1, pp. 1–21, 2016, doi: 10.22452/ijps.vol6no1.2.
  13. A.Nur, R. Ema, H. Taufiq, and W. Firdaus, “Modeling House Price Prediction using Regression Analysis and Particle Swarm Optimization Case Study: Malang, East Java, Indonesia,” Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 10, pp. 323–326, 2017, doi: 10.14569/ijacsa.2017.081042.
  14. A.Yusof and S. Ismail, “Multiple Regressions in Analysing House Price Variations,” Commun. IBIMA, vol. 2012, pp. 1–9, 2012, doi: 10.5171/2012.383101.
  15. A.Osmadi, E. M. Kamal, H. Hassan, and H. A. Fattah, “Exploring the elements of housing price in Malaysia,” Asian Soc. Sci., vol. 11, no. 24, pp. 26–38,2015, doi: 10.5539/ass.v11n24p26.
  16. T. L. Chin and K. W. Chau, “A critical review of literature on the hedonic price model,” Int. J. Hous. Sci. Its Appl., vol. 27, no. 2, pp. 145–165, 2003.
  17. M. J. Ball, “Recent Empirical Work on the Determinants of Relative House Prices,” Urban Stud., vol. 10, no. 2, pp. 213–233, 1973, doi: 10.1080/00420987320080311.
  18. Jebashiniponnian Senthil Pari Uma Ramadass Chee Pun Ooi “A Unified Libraries for GDI Logic to Achieve Low-Power and High-Speed Circuit Design” Dec 2022.