This document summarizes a project that aims to predict restaurant ratings and changes in popularity on Yelp using machine learning models. The project uses data from the Yelp Dataset Challenge including information about restaurants like location, prices, food types, and reviews. Logistic regression performs better than naive Bayes, neural networks, and support vector machines at predictions, but all models' predictions are imperfect, suggesting room for improvement. The goals are to predict ratings and popularity changes based on restaurant features to provide suggestions for new restaurants.

1. Predicting Restaurants Rating and Popularity
based onYelp Dataset
Presented by : Alin Babu(67)|Nandu O(66)|Liju Thomas(36)

2. Abstract
Restaurants rating on Yelp becomes an important indicator of their
future. In this project, we focus on predicting ratings and popularity
change of restaurants. With data from Yelp, we use several machine
learning methods including logistic regression, Naive Bayes, Neural
Network, and Support Vector Machine (SVM) to make relevant
predictions. While logistic regression seems to perform better than the
others, predictions from all the methods are far from perfect. This
implies the potential improvement of more data and more suited
methodology.

3. Project Goals
 To predict ratings of restaurants on Yelp and popularity change based
on restaurant features.
 Project can shed lights on what customers value the most about a
restaurant.
 Provide suggestions on what feature combinations one should choose
when opening a new restaurant, and how likely this restaurant can
succeed.

4. Input
 Uses Yelp dataset
 The input to the algorithm is related feature variables about a
restaurant, such as :
– price range
– noise level
– service available
– food type
– location
– opening hours etc.

5. Algorithms and Methods
 Linear Regression
 Naive Bayes
 Neural Network
 Support Vector Machine

6. Dataset
 The data comes from Yelp Dataset Challenge .
 It is a small subset of Yelp data, including information about local businesses in 12
metropolitan areas across 4 countries.
 The data contains information such as location, opening hours, price level, food
type, service provided etc.
 It also includes review data, including text, time and rating.
 From the raw dataset, we select 74 related features.
 Due to different cultures across cities, we only focus on restaurants in Toronto and
surrounded areas in this project.

7. What we can do in this project
 Predicting restaurants rating.
 Predicting popularity of a restaurant
 Predicting is based on data from restaurants in Toronto, a city
representing a variety of cuisine choices as our main target.

8. What we can’t do in this project
 Prediction is not based on a user.
 Not sure about implementation Multinomial Logistic Regression.
 Does not use any other data other than Yelp.