抽象的な

Predicting Ratings of Online Food Chain

Srishty Sri Nidhi, Ravi Shankar Pandey

Online food ordering is going to be popular day by day and it requires customer satisfaction for more popularity in the society Several online food ordering System are available on internet like Zomato, Swiggy, Fresh menu, Dunzo, Guruhub, EatSure, UberEats, Deliveroo, dominos etc. All such kind of system requires customer satisfaction in the form of the feedback mechanism. This feedback mechanism helps to provide Appropriate food at location on the basis of customer rating.

In this paper we have analysed data of Zomato to incorporate location wise customer satisfaction to provide better restaurant for food ordering to customer.

We have used machine learning linear regression technique to separate better restaurant on the basis of customer satisfaction rating. We will also use this algorithm to predict aggregate ratings restaurants will receive based on different data points. We have tested our algorithm using Kaggle dataset.