Location Profiling for Retail-Site Recommendation Using Machine Learning Approach
- 10.2991/978-94-6463-094-7_5How to use a DOI?
- Retail-Site-Recommendation; Feature Selection; Machine Learning Model
Retail site selection is a critical stage for a new retailer since it helps them to decide which locations have the best chance of delivering a good return on investment. Most of the new retailers will face problems while selecting a retail site for new business. Work presented in this paper will focus on predictive modelling by using the geographical variables and demographic variables. Besides, an analytical dataset will be constructed that generated by several algorithm and the five different feature selection will be performed on the analytical dataset to increase the efficiency of models. There are six classification models were developed in this project, which is Random Forest Classifier, XGBoost Classifier, Logistic Regression, Naive Bayes Classifier and Decision Tree Classifier. Besides, a deep learning classification models will be developed in this project, which is Multi-layer Perceptron Classifier. Accuracy, Precision, Recall, and F1-Score are used to evaluate the performance of classification models in this project. Among all models that constructed by using different features of several feature selection, XGBoost Classifier has the highest accuracy, which is around 94%.
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Cite this article
TY - CONF AU - Choo-Yee Ting AU - Mang Yu Jie PY - 2022 DA - 2022/12/27 TI - Location Profiling for Retail-Site Recommendation Using Machine Learning Approach BT - Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022) PB - Atlantis Press SP - 48 EP - 67 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-094-7_5 DO - 10.2991/978-94-6463-094-7_5 ID - Ting2022 ER -