Zomato Data Analysis and Restaurant Recommendation-Reference-Cited by-同舟云学术

Zomato Data Analysis and Restaurant Recommendation

Published:2023-08-31 Issue: Volume: Page:872-878
ISSN:2581-9429
Container-title:International Journal of Advanced Research in Science, Communication and Technology
language:en
Short-container-title:IJARSCT

Author:

Chethas Anil Reddy ¹,Darshan L ¹,Gagan Nayaka M N ¹,Harsha Jayendra Varma P ¹,Mamatha V ¹

Affiliation:

1. Bangalore Institute of Technology, Bengaluru, India

Abstract

The main aim of this project is to develop an accurate model and compare different models to recommend restaurants to users. This is done by analyzing the impact of various factors that are obtained from the ’Bangalore Zomato dataset’ and affect the decision of a customer. Data pre-processing and visualization is performed to understand the attributes of the dataset and make decisions for building the model. TF-IDF is used for the vectorization of text data in the attributes and models with different distance measures are built and compared with each other. Keras Embedding Layer is used to create word embeddings for text data, therefore, generating continuous numeric data for every restaurant. This numeric data of each restaurant is compared with every other restaurant and ’k’ most similar restaurants are recommended

Publisher

Naksh Solutions

Subject

General Medicine

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