KNN Based Personalized Dietary Advisor
Keywords:
Class imbalance, Explainable AI, Feature selection, Hyperparameter tuning, K-Nearest Neighbors (KNN), Machine learning, Nutritional optimization, One-hot encoding, Personalized diet recommendation, South indian meal preferencesAbstract
In order to achieve a variety of fitness and health objectives, such as weight loss, muscular growth, diabetes control, and maintenance, personalized diet is essential. Conventional dietary guidelines are frequently not tailored to each individual, which produces less than ideal outcomes. This study creates a personalized dietary advisor that creates meal plans according to dietary preferences, health problems, activity level, age, and gender using K- Nearest Neighbors (KNN) with one-hot encoding. South Indian food preferences are included in the dataset, guaranteeing cultural relevance and diversity in meal recommendations. By use of feature encoding and scaling, the model efficiently handles both numerical and categorical inputs, attaining 76% prediction accuracy in nutritional recommendations. With its data-driven approach to enhancing fitness and health outcomes, this system improves nutrition personalization by providing real-time, user-specific meal planning.
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