CMU Interactive Data Science Final Project

Online URL: https://share.streamlit.io/cmu-ids-2022/final-project-mass-squad/sidharth
Video https://drive.google.com/file/d/1bTqD6tvRUv3YvHJ8fSDqaEm1GztDpX_Y/view?usp=sharing
Team members:
- Contact person: anjadhav@andrew.cmu.edu
- skathpal@andrew.cmu.edu
- mvijay@andrew.cmu.edu
- shreyas3@andrew.cmu.edu

Overview

With the evolution of companies such as Doordash and Uber Eats, customers have a wide gamut of choices of restaurants at their fingertips with handy access to millions of reviews and ratings and cuisines. It is very easy for customers to filter by locality, cuisines and ratings to shortlist a restaurant to visit or order from. Given such wide options, restaurants need to ramp up their amenities and food offerings in order to get more favourable reviews and ratings and attract even more customers. In this situation, they have little options to analyze salient aspects of their business, find out what is lacking and what else can be improved. Moreover, for newbies in the restaurant industry, there are limited avenues for comprehensively analyzing the industry’s landscape and making decisions about what cuisines to serve or what amenities to offer. Motivate by this lack of a comprehensive analysis tools for restaurant owner’s we develop this application that processes millions of yelp restaurant reviews, ratings and attributes and offers an array of interactive visualizations to aid restaurant owners make the right decisions for their business.

Work distribution

The contribution of each team member is listed below -

Aishwarya Jadhav - data preprocessing, map visualization
Sidharth Kathpal - data preprocessing, overall rating distribution visualizations, deployment of the app to streamlit, maintainence
Shreya Sharma - data preprocessing, word cloud, rating distribution for individual restaurants
Malaika Vijay - data preprocessing, similarity score computation, sentiment analysis, visualization comparing the ratings of businesses over time

How to Run

Environment Setup

To set up the environment, run pip install -r requirements.txt

Data Preparation

Download the reviews and business json data from this link - Yelp Dataset
Preprocess the business json data by running python business_preproc.py --in-file <path_to_business_json> --out-file <path_to_output_csv>
Preprocess the reviews json data by running python reviews_preproc.py --in-file <path_to_reviews_json> --out-file <path_to_output_csv> --business-file <path_to_preprocessed_business_csv>

Run the Streamlit Application

To run the streamlit application, run streamlit run streamlit_app.py

Deliverables

Proposal

The URL at the top of this readme needs to point to your application online. It should also list the names of the team members.
A completed proposal. Each student should submit the URL that points to this file in their github repo on Canvas.

Sketches

Develop sketches/prototype of your project.

Final deliverables

All code for the project should be in the repo.
Update the Online URL above to point to your deployed project.
A detailed project report. Each student should submit the URL that points to this file in their github repo on Canvas.
A 5 minute video demonstration. Upload the video to this github repo and link to it from your report.

CMU-IDS-2022/final-project-mass-squad