• Parth Gangdev
• Siddhi Shukla
• Pranjal Naik
• Manish Rana

The main objective behind developing this project is to build a restaurant recommendation system for an individual based on their preference. We create a ranking SVM model using the machine learning algorithm with features encompassing of the parameters such as food preferences, cuisine type, staff service, ambiance, noise level, average rating, and dietary restrictions etc. The ability of the algorithm and recommendation engines to recommend personalized content, based on past behavior is what we majorly aim to develop. The personalized experience is what makes the user return to the website.Users do not rate adequate restaurants to enable collaborating filtering based recommendation, which can lead to an issue. To solve this issue a hybrid approach is used where collaborative filtering technique aggregates with the content based algorithm, to provide personalized restaurant recommendation. Here Hybrid approach in Machine Learning is performed where weights of each aspect is calculated to find its orientation score. Also we use K Nearest Neighbor algorithm to group the aspects of similar meaning. Thus, content-based and collaborative approach increases the recommendation results. We also use Flask to connect the data sets to the python and finally display the output.

Recommender system, K Nearest Neighbor, Content based and Collaborative based Machine Learning, Flask.

"Design and development of Restaurant Recommendation system using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.7-12, April 2019, Available :http://www.jetir.org/papers/JETIR1904D02.pdf

"Design and development of Restaurant Recommendation system using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp7-12, April 2019, Available at : http://www.jetir.org/papers/JETIR1904D02.pdf