Food Culture Analysis in Bengaluru
DOI:
https://doi.org/10.61841/tv0zyx24Keywords:
Restaurant, Prediction, Restaurants, XGBoostAbstract
Bangalore is a city well known for its culture and food. There are nearly ten thousand restaurants across the entire city of Bangalore. These restaurants sometimes charge exorbitantly despite poor reviews. As per the data captured from Zomato, a food ordering app, we studied the factors like location, ratings, menu diversity, etc., majorly affecting a restaurant. The price to be charged was then predicted to realize overpricing or underpricing by comparing it with the actual price. The prediction problem is very important to assess the prices and preferences among people.
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[1] S. Jhaveri, I. Khedkar, Y. Kantharia, and S. Jaswal, 2019. Success Prediction using Random Forest, CatBoost, XGBoost, and AdaBoost for Kickstarter Campaigns. 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 1170-1173.
[2] X. Ma, Y. Tian, C. Luo, and Y. Zhang, 2018. Predicting Future Visitors of Restaurants Using Big Data. International Conference on Machine Learning and Cybernetics (ICMLC), Chengdu, 2018, pp. 269-274.
[3] U. Vanichrujee, T. Horanont, W. Pattara-atikom, T. Theeramunkong, and T. Shinozaki, 2018. Taxi Demand Prediction Using an Ensemble Model Based on RNNs and XGBOOST. International Conference on Embedded Systems and Intelligent Technology & International Conference on Information and Communication Technology for Embedded Systems (ICESIT-ICICTES), Khon Kaen, 2018, pp. 1-6.
[4] A.L. Xu, B.J. Liu, and C.Y. Gu, 2018. A Recommendation System Based on Extreme Gradient Boosting
Classifier. 10th International Conference on Modelling, Identification and Control (ICMIC), Guiyang,
2018, pp. 1-5.5.
[5] G. Cao, A. Downes, S. Khan, W. Wong, and G. Xu, 2018. Taxpayer Behavior Prediction in SMS Campaigns, 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC), Kaohsiung, Taiwan, pp. 19-23.
[6] K.D. Kankanamge, Y.R. Witharanage, C.S. Withanage, M. Hansini, D. Lakmal, and U. Thayasivam, 2019. Taxi Trip Travel Time Prediction with Isolated XGBoost Regression. Moratuwa Engineering Research Conference (MERCon), pp. 54-59.
[7] X. Shi, Q. Li, Y. Qi, T. Huang, and J. Li, 2017. An accident prediction approach based on XGBoost, 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Nanjing, pp. 1-7.
[8] W. Qiu, 2019. Credit Risk Prediction in an Imbalanced Social Lending Environment Based on XGBoost, 5th International Conference on Big Data and Information Analytics (BigDIA), Kunming, China, 2019,p. 150-156.
[9] M. Gumus and M.S. Kiran, 2017. Crude oil price forecasting using XGBoost, International Conference on Computer Science and Engineering (UBMK), Antalya, 2017, pp. 1100-1103.
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