Personalized Indian Food Recommendation System based on region and course

Abstract

The rapid development of technology and the rising popularity of internet platforms have fundamentally changed how people explore and learn about different cuisines. A growing market exists for effective and personalised meal recommendation systems that take into account individual tastes in this digital age. The design and implementation of an Indian food recommendation system are presented in this thesis, with an emphasis on offering specialised recommendations. The suggested Indian cuisine recommendation system examines user preferences and behavioural trends using machine learning and data mining approaches. The technology creates personalised recommendations for Indian cuisine by taking into account elements like taste preferences, dietary limitations, cultural backgrounds, and previous user interac tions. The goal is to increase customer pleasure and promote the discovery of various Indian foods. The system uses an extensive Indian cuisine database that includes recipes, ingredi ents, cooking techniques, and regional variants to do this. It also incorporates user reviews and ratings to generically raise the quality of recommendations. To provide a varied col lection of recommendations and guarantee a fair representation of various Indian cuisines, content-based filtering and hybrid recommendation algorithms are used in this case. To evaluate the effectiveness of the system, extensive user studies and experiments are conducted. Users are requested to rate and provide feedback on the recommended dishes based on their personal experiences. The results are analyzed using statistical methods to measure system performance, including accuracy, precision, and user satisfaction.