To find a popular location for opening a new shop is beneficial for the achievement of a future business. The Various Surveys by people and the analytics based models are based on statistics data which are very time consuming and not user friendly to the dynamic market. Due to increase of data from different electronic media such as online data inquiry and offline positioning survey, there is requirement to introduce the accurate and automatic forecasting model for real time prediction of a location for business purpose. In Real Time Location Prediction (RTLP), we model a frame of reference for location prediction for business store site selection by rational and historical data of customer location. This detects the customer demand distribution location for various business services and taking dataset from Google maps ( it is the greatest online mapping service in India) .It also found the gaps between demand and supply in various business services. Then, we determine the customer locations through clustering. At the end we find the solution for location optimization problem using customer location. We not only use unsupervised Machine Learning (ML) models to predict the density of customers, but here we find the nearest customer to that shop. We estimate our framework on various types of real world business problems on site location detection and determine the accuracy of results of our methods. The experimental results of the proposed methods provide good shop location with commendable processing speed (4.49 Seconds).
Volume 12 | 08-Special Issue
Pages: 81-93
DOI: 10.5373/JARDCS/V12SP8/20202504