Sales Prediction for Pizza Restaurant Using Historical Order Data

Abstract

In the food industry, sales forecasting is a crucial factor for the growth of the business. The inventory control, waste reduction, customer satisfaction and other derivative problems hang directly upon our ability for future sales accurate forecasting. The main aim of this research is to find out which machine learning model is most effective for predicting pizza sales historically based on orders. Several advanced machine learning techniques are used in this research, including Linear Regression, Decision Tree, Random Forest, Gradient Boosting and Multi-Layer Perceptron (MLP) Regressor. After the data is processed, the first thing that occurs is a minimalist preprocessing stage. This phase includes removing inappropriate data, handling missing entries and carrying out feature engineering to capture more favorable conditions for the models so that it can predict accurately. Selecting characteristics not only helps define ways to better segment model training but also could influence how well built the model is. To do this, by employing feature selection the most closely related variables that lead to pizza sales are filtered in order to guarantee that the models are both accurate and efficient. Following the preprocessing stage, the selected machine learning models are trained on the dataset. To assess the effectiveness of each model, we use several key performance indicators. These include Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and R-squared (R²). These indicators provide a comprehensive assessment of both the models' predictive accuracy and their potential for going into new environments in the future. The evaluation results show that there are major differences in performance among models, with Gradient Boosting emerging as the best performing model. The Gradient Boosting model had the lowest error margins, and the best overall fit to the data, making it the most suitable model for predicting pizza sales in this context. The results of this study have significant implications for the restaurant 's operations strategy. With its adoption of the Gradient Boosting model, we can expect the restaurant to make more accurate sales predictions, leading to heightened inventory turnover, reduced waste and improved customer satisfaction. Having more accurate sales forecasts also enables the restaurant to take staffing, promotions and menu planning in an analytical direction. Moreover, it raises overall efficiency and earnings. This study is a positive addition to the broader area of food industry predictive analytics. It demonstrates that advanced machine learning models could help solve real-world sales forecasting problems in food industry situations. The study also indicates future possible directions such as developing more precise algorithms, collecting more data and using other data sources like weather or events in conjunction with sales data sets, applying these models to other areas of a restaurant operation. The successful implementation of these predictive models can serve as a foundation for continuous improvement in sales forecasting and decision-making processes within the restaurant industry.