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Online Grocery Recommendation System Implementation Using Python Flask and Machine Learning

Manjushree Nayak, Subhakanta Panda

Abstract


This paper describes the creation and deployment of a sophisticated online grocery recommendation system that makes use of machine learning techniques using Python Flask. E-commerce is growing at an exponential rate, and this is especially true for the food industry. To improve consumer satisfaction, personalised and effective recommendation systems are essential. The suggested method makes use of cutting-edge machine learning techniques to examine past data, user preferences, and in-the-moment interactions in order to provide precise and customised grocery recommendations. The web framework used in the implementation, Python Flask, gives the recommendation system a stable and expandable base. The system automatically adjusts to user behaviours by integrating content-based filtering algorithms and collaborative filtering, guaranteeing fast and relevant suggestions. The user interface is made to be simple to use and integrate seamlessly.

 


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References


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