NourishNook: Home Remedies and Personalized Health Recommendations using NLP

NourishNook is an AI-based health advisory system providing individualized wellness recommendations through advanced machine learning techniques. On the basis of eight comprehensive datasets of symptoms, illnesses, precautions, and nutrition suggestions, the system provides accurate and user-centric health guidance. The website features a multi-level design in which inputs received from the users are processed based on prediction models such as Support Vector Machines (SVM), Random Forest Classifier, Gradient Boosting, K-Nearest Neighbors (KNN), and Multinomial Naïve Bayes in order to enhance the diagnostic precision. Based on the predicted condition, a recommendation algorithm proposes appropriate remedies, precautions, and diet plans so that users can take apt health decisions. Developed with strong backend guaranteed by Flask, MySQL, and basic Python libraries, NourishNook ensures smooth real-time data management and AI-supported insights. Being data authenticity and security as well as user-access centric in its design, the system may prove to be an effective replacement for traditional health advisory services. Future improvements will see the expansion of the dataset, the improvement in model performance, and the integration of multilingual capabilities to better provide accessibility and usability for various populations.