ml-yelp's Introduction

Data science project about Yelp dataset

files provided : html version of the jupyter lab

Research key question : to what extent Yelp is a powerful indicator ? A study case in Arizona

Content summary :

Study of the differences of number of reviews versus given stars per locations and types of businesses.
Identification of a specific study case where this ratio shows pre-patterns regarding key question.
Merge with the reviews provided by Yelp
Study all the new features toward the main open/close target and create new meaningful features on a spatial and time basis
Study the review contents and any potential misclassification
Predicting the open/close binary. How well Yelp information can lead to a prediction of a closing restaurant?

Methods used thoughout the study:

Efficiently gathering Yelp keywords into meaningfull business categories :
- Google pre trained model of Word2Vect
- Clustering kmeans on cosine similiarities
Using the standard and robust metics to evaluate the randomness of up-rated and down-rated restaurants
Extracting insights from a spatial and temporal breakdown :
- Maps and Haversines distances
- Analysing frequencies of the rating
Analysing reviews
- WordCloud per star
- Sentiment analysis through emoticons and Polarity values
Identifying any grade missclassification
- Tf-idf matrix on reviews versus This matrix with binary emoticon sentiment
- Multinomial Naive Bayes versus Support Vector MAchine with a linear Kenerl
Studying the Yelp impact on the open or close business
- Tsne visualization
- Correctly checking correlation between continuous and discrete variables
- Logistic Regression and Random Forest

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