NUTRITIONAL VALUE PREDICTION IN FOOD RECIPES USING MACHINE LEARNING AND DATA ANALYTICS

Abstract

The prediction of nutritional value in food recipes is crucial for promoting healthy eating habits and personalized dietary recommendations. This research paper presents a comprehensive approach to predicting the nutritional content of food recipes using machine learning and data analytics. The study utilizes a dataset containing detailed information about ingredients, cooking methods, and nutritional values. Machine learning algorithms, including regression models, decision trees, and neural networks, are employed to develop predictive models for estimating calories, macronutrients, vitamins, and minerals in diverse recipes. Feature engineering techniques are applied to extract relevant nutritional information from raw ingredients, while advanced data preprocessing methods ensure the accuracy and reliability of the predictions. The proposed approach is validated through extensive experiments on a diverse set of recipes, demonstrating high prediction accuracy across various nutritional parameters. Furthermore, the integration of data analytics enables the identification of key ingredients and cooking methods that significantly impact the nutritional profile of recipes. The study also explores the potential of personalized nutrition by tailoring the predictions to individual dietary preferences and health goals. This research contributes to the growing field of food informatics by providing an effective tool for nutritional analysis, which can be integrated into mobile applications, online platforms, and healthcare systems. The findings highlight the importance of leveraging machine learning and data analytics to enhance the accuracy of nutritional value predictions, ultimately supporting healthier food choices and improved public health outcomes.