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By utilizing machine learning, the current research offers a fresh approach to food demand forecasting in restaurants. It focuses on store-specific models that take into account a variety of variables, including location, weather, and events. Conventional methods frequently ignore store-specific subtleties in favor of relying exclusively on point-of-sale (POS) data. We provide a food demand forecasting model that utilizes machine learning methods, specifically XGBoost and CatBoost, to incorporate various data sources. The study goes into how the model was created, how real shop data was used to validate it, and how it was used to assess variables that affect consumer happiness, especially in the context of the fast-food trend. The study explores the variables influencing preferences for fast-food outlets and offers insights into consumer satisfaction and how it affects the fast-food restaurant industry as a whole. The benefits of the presented hypothesis, along with the algorithms and libraries used (Pandas, NumPy, Scikit-learn, and CatBoost) are described in depth. The study also emphasizes the significance of data visualization, ensemble techniques, hyperparameter adjustment, and other gradient-boosting libraries like XGBoost and CatBoost. Libraries for time series analysis are also thought to be useful for capturing data's temporal patterns. This all-encompassing method helps to create a strong and effective framework for food demand forecasting, which is crucial for restaurant management and optimization.

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An Efficient Approach for Food Demand Forecasting Using an Ensemble Technique and Statistical Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Dudla Anil kumar,
  • Bathula Thirupathi Rao,
  • Bathini Rangaswamy,
  • Kagitha Meghana

摘要

By utilizing machine learning, the current research offers a fresh approach to food demand forecasting in restaurants. It focuses on store-specific models that take into account a variety of variables, including location, weather, and events. Conventional methods frequently ignore store-specific subtleties in favor of relying exclusively on point-of-sale (POS) data. We provide a food demand forecasting model that utilizes machine learning methods, specifically XGBoost and CatBoost, to incorporate various data sources. The study goes into how the model was created, how real shop data was used to validate it, and how it was used to assess variables that affect consumer happiness, especially in the context of the fast-food trend. The study explores the variables influencing preferences for fast-food outlets and offers insights into consumer satisfaction and how it affects the fast-food restaurant industry as a whole. The benefits of the presented hypothesis, along with the algorithms and libraries used (Pandas, NumPy, Scikit-learn, and CatBoost) are described in depth. The study also emphasizes the significance of data visualization, ensemble techniques, hyperparameter adjustment, and other gradient-boosting libraries like XGBoost and CatBoost. Libraries for time series analysis are also thought to be useful for capturing data's temporal patterns. This all-encompassing method helps to create a strong and effective framework for food demand forecasting, which is crucial for restaurant management and optimization.