Analyzing Healthiness of Fast Food Items Using statistics and Machine Learning

This paper presents a comprehensive approach to evaluating the healthiness of fast food items through the integration of statistical analysis and machine learning techniques. By utilizing a dataset containing detailed nutritional information from multiple fast food chains, a custom health scoring algorithm is developed to assess and compare the nutritional quality of individual food items. Key health indicators such as calories, fat content, sugar, protein, and dietary fiber are analyzed to compute a standardized health score. In addition, a cancer risk model is implemented to estimate potential health risks associated with the consumption of specific items, emphasizing the importance of dietary choices in mitigating cancer risk. The use of machine learning techniques enhances the predictive capability of the model, allowing for the identification of patterns and trends across different restaurant chains. The proposed methodology offers valuable insights for consumers seeking healthier fast food options and provides a data-driven foundation for public health initiatives aimed at improving dietary habits. The findings demonstrate the significant nutritional variation among fast food items and highlight the potential for machine learning to contribute to nutrition-based public health research.