Location Prediction Models using Data Mining and Machine Learning
Chetashri Bhadane1, Ketan Shah2, M. A. Khatkhatay3, A. M. Darukhanawalla4
1Chetashri Bhadane*, Assistant Professor, Department of Computer Engineering, Dwarkadas J. Sanghvi College of Engineering, Mumbai, India.
2Mohammed Aqid Khatkhatay, Department of Computer Engineering, Dwarkadas J. Sanghvi College of Engineering, Mumbai, India.
3Dr. Ketan Shah, Professor, SVKM’s MPTSME, Mumbai, India.
4Aamir Murtuza Darukhanawalla, Department of Computer Engineering, Dwarkadas J. Sanghvi College of Engineering, Mumbai, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 3383-3390 | Volume-9 Issue-3, February 2020. | Retrieval Number: C6044029320 /2020©BEIESP | DOI: 10.35940/ijeat.C6044.029320
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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: A vast availability of location based user data which is generated everyday whether it is GPS data from online cabs, or weather time series data, is essential in many ways to the user and has been applied to many real life applications such as location targeted-advertising, recommendation systems, crime-rate detection, home trajectory analysis etc. In order to analyze this data and use it to fruitfulness a vast majority of prediction models have been proposed and utilized over the years. A next location prediction model is a model that uses this data and can be designed as a combination of two or more models and techniques, but these have their own pros and cons. The aim of this document is to analyze and compare the various machine learning models and related experiments that can be applied for better location prediction algorithms in the near future. The paper is organized in a way so as to give readers insights and other noteworthy points and inferences from the papers surveyed. A summary table has been presented to get a glimpse of the methods in depth and our added inferences along with the data-sets analyzed.
Keywords: Context, mobility, next-location prediction, trajectory.