Machine Learning Approach for Best Location of Retailers

This paper presents a machine learning approach using the k-means clustering algorithm to identify optimal locations for retailers. The study aims to leverage geographic, demographic, and economic factors to cluster potential locations and provide valuable insights for decision-making. The methodology involves data preparation, selecting relevant features, applying the k-means algorithm, evaluating cluster results, and visualizing the outcomes on a map. Numerical results demonstrate the effectiveness of the proposed approach in identifying suitable retail locations. The study concludes with a summary of findings and recommendations for further research.This paper presents a machine learning approach using the k-means clustering algorithm to identify optimal locations for retailers. The study aims to leverage geographic, demographic, and economic factors to cluster potential locations and provide valuable insights for decision-making. The methodology involves data preparation, selecting relevant features, applying the k-means algorithm, evaluating cluster results, and visualizing the outcomes on a map. Numerical results demonstrate the effectiveness of the proposed approach in identifying suitable retail locations. The study concludes with a summary of findings and recommendations for further research.

Keywords:

Machine Learning , Location , Retailers , Clustering , K-means