Food Sales Analysis and Prediction Using Machine Learning

This study uses Support Vector Machines (SVM) with Positive and Negative Binomial distributions to give a thorough analysis and prediction model for food sales. The project's goal is to improve sales forecasting's resilience and accuracy in the dynamic and intricate food business. When analyzing historical sales data, the SVM algorithm is used to identify complex patterns and correlations that take into account variables like seasonality, promotional activities, and outside influences. Furthermore, the model's integration of the positive and negative binomial distributions allows it to take into consideration excess zeros and overdispersion, which are frequently seen in datasets related to food sales. Food sales data are used to assess the suggested methodology, showing how well it captures the underlying distributional characteristics and yields precise forecasts. The results further the field of predictive analytics in the food industry by providing a dependable instrument for companies to plan marketing campaigns, optimize inventory management, and improve overall operational efficiency.