• Mr.Tarunya Vairagade
• Mr.Prajyot Jadhao
• Ms.Kanishka Mankar
• Mr.Rahul Kadam
• Prof.Rohan Kokate.

The Drug Recommendation System presented in this project harnesses the synergy of Machine Learning (ML) and Natural Language Processing (NLP) to deliver personalised and context-aware suggestions for pharmaceutical interventions. By amalgamating data from diverse sources, including drug properties, medical conditions, and patient reviews, the system establishes a comprehensive knowledge base. Initial data preprocessing involves advanced NLP techniques, facilitating sentiment analysis and extracting meaningful insights from unstructured textual data. The ML component employs a hybrid model that combines collaborative filtering and content-based filtering, ensuring the accuracy and personalization of drug recommendations. The user interface is designed for simplicity, allowing users to input medical information and preferences, supported by visualisation tools that provide detailed insights into recommended drugs. A continuous feedback loop enhances the system's adaptability, evolving based on real-world feedback and user experiences. This project signifies a forward-looking approach to healthcare solutions, leveraging ML and NLP to create a dynamic and user-centric Drug Recommendation System.

Medicine Recommendation System, Personalized Healthcare, Data Management, Scalability, Treatment Effectiveness ,Machine learning, Natural Language processing.

"A Review On Drug Recommendation System Using ML and NLP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.f707-f710, April-2024, Available :http://www.jetir.org/papers/JETIR2404580.pdf

"A Review On Drug Recommendation System Using ML and NLP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppf707-f710, April-2024, Available at : http://www.jetir.org/papers/JETIR2404580.pdf