What machine learning models are used for retail site selection?

Various machine learning models are employed for retail site selection, with each offering unique capabilities for analyzing complex datasets.

•Regression models (e.g., Linear Regression, Random Forest Regressor) predict sales volumes or customer counts based on location features.

•Classification models (e.g., Logistic Regression, Support Vector Machines) identify locations likely to succeed or fail.

•Clustering algorithms (e.g., K-Means, DBSCAN) group similar geographic areas to identify underserved markets or optimal territories.

•Neural Networks, particularly deep learning models, can uncover intricate, non-linear relationships between location attributes and performance.

•Geospatial models and spatial statistics are integrated to account for the unique geographical dependencies of retail data.

WhereToOpen.ai utilizes an ensemble of these powerful ML models to provide precise retail site recommendations.