The aim of the project is to provide valuable insights to aspiring restaurant owners to set up their restaurant in such a way that it increases their probability of success. This is based on three key pieces of information: the most influential features that determine high restaurant ratings, location distribution of their competitors, and customers' experiences of those through review sentiment mining.
Guide to setup and run the code can be found here RUNNING.md
With the below code we want the model to learn how the facilities of a restaurant affect its ratings. We feed 15 attributes/features to the model and predict the rating against the ratings provided by Yelp. This could be used to predict the initial ratings of a new restaurant based on the facilities it provides. In this model, we find out that features like WheelchairAccessible , DogsAllowed are given more weightage while predicting the rating. We get an accuracy of ~82%.
The Tableau Workbook Yelp_Data_Analysis.twb is already connected to the CSV files that were created during the ETL steps.
Dietary Restriction Dashboard gives the number of restaurants that offer food for people with dietary restrictions such as gluten allergy, lactose intolerance etc. and also plots their location on a map.
Restaurant Facilities Dashboard shows for each state, the total number of restaurants available for various kinds of services. This can further be drilled down for each city.
An app that let's you look up similar businesses based on "categories" column in business.json data file. Once similar businesses are found, it quickly plots them on a map for the user to view it. Head to the app folder for more details.
When a user is undecided on what restaurant/cuisine to open for a business opportunity this app can help you decide that. Just look up "Sushi" in Vancouver around 15kms and it will show all businesses that have 'sushi' or find similar business category if 'sushi' is not available.
What if given category is not available in data? Fret not, the app uses GloVe embeddings to first map user provided attribute to an embedding and then it uses cosine similarity to find most similar businesses in case the user provided attribute/category is not available in categories column.
Positive and negative reviews from top restaurants in a given region were mined to get insights about what customers of these establishments are feeling about their experience. This is determined by the frequency in which sequence of words (ngrams) appear in the reviews. A sentence sentiment based approach was used in which an overall positive, negative and neutral sentiment was assigned to each sentence using the Afinn Lexicon. This way, even when the overall review is rated as positive or negative, more information about what aspects within the review, customers felt positive, negative or neutral about. Thus, adding an additional layer of understanding.
The frequency table of ngrams can be visualized as a histogram. Below is the visualization of the ngram count of the top 5 pizza restaurants with more than 100 reviews grouped by review (positive or negative) and sentiment (positive, negative or neutral)
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