Zomato-Restaurant-Clustering-and-Sentiment-Analysis

This project entailed the utilization of advanced data analytics techniques to gain a deeper understanding of the restaurants and customer feedback on the popular online food delivery platform, Zomato.

The data procured included information such as the restaurant's name, location, cuisines, average cost for two, ratings, and user reviews.

I implemented clustering on the restaurant data through the use of the k-means algorithm. The objective of the clustering was to group similar restaurants together and discern patterns within the data.

I then proceeded to conduct sentiment analysis on the user reviews to gain a comprehensive understanding of the overall sentiment towards the restaurants.

In conclusion, this project exemplifies the utility of clustering and sentiment analysis in gaining a more profound comprehension of restaurant data on Zomato.