Comparative analysis of machine learning techniques in optimal site selection

Abstract

Site selection is a crucial aspect of many businesses, as a company’s location can sig nificantly impact its success. In recent years, machine learning techniques have been increasingly used to assist with optimal site selection by providing data-driven pre dictions about the potential success of a given location. Machine learning techniques can be used to assist in the process of selecting the optimal site by analyzing the patterns in data such as demographics, lifestyle services, and geographic features. In this paper, we compare several machine learning techniques for their perfor mance in optimal site selection for features extracted from Open Street Map (OSM) data, WorldPop population data, and Bing satellite imagery. A target dataset cor responding to the features extracted was collected from Yelp data on restaurant check-ins, and this was used as a parameter to determine the human engagement rate of that location with the businesses in that area. Our analysis methods in cluded SVR, Random Forest, XGBoost, Ridge Regression, Lasso Regression, and ElasticNet. The satellite imagery collected from Bing maps were used to train CNN architectures such as; VGG16, VGG19, ResNet, DenseNet, and InceptionV3 and the results were compared. We evaluated the techniques using several metrics, including Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), Median Absolute Error(MedAE), Max Error(ME), and Median Abso lute Deviation(MAD). We used algorithm and strategies that performed the best in related works for this research. One meta model was also implemented in this work by an ensemble learning technique known as stacking. The model that performed the best for the data collected was then determined by looking at the error scores of different models. This work provides an insight into the strengths and limitations of each technique and recommendations for practitioners considering the use of ma chine learning in site selection. This study demonstrates the potential of machine learning for improving site selection processes and highlights the importance of con sidering multiple approaches.