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"Yelp is a platform for reviews of all types of businesses, restaurants, hotels, services, among others. Users use the service and then upload their review based on the experience they have received. This information is very valuable for companies, since it gives them It serves to find out the image that users have of the different company premises, being useful to measure the performance, usefulness of the premises, in addition to knowing in which aspects the service must be improved."
"As part of a data consultancy, we have been hired to perform an analysis of the US market. Our client is part of a conglomerate of restaurant and related companies, and they want to have a detailed analysis of the opinion of the users on Yelp about hotels, restaurants and other businesses related to tourism and leisure, using sentiment analysis, to predict which business items will grow (or decline) the most.In addition, they want to know where it is convenient to locate the new restaurants and related premises, and They want to be able to have a restaurant recommendation system for Yelp users to give the user the ability to be able to learn about new flavors based on their previous experiences.They can change the type of business (doesn't have to be restaurants). "
You can read the full instructions here
| Role | Name | Github |
|---|---|---|
| Data Analyst | Lila Alves | @LilaAlvesDC |
| Data Engineer | David Duarte | @acidminded95 |
| Data Engineer | Julieta Ciare | @julieta77 |
| Data Manager | Thiago Ferster | @CodeKova |
| Data Scientist | Maico Bernal | @maicobernal |
You can read the full data structure analysis here
Focus mainly on data exploration, tech stack to implement with emphasis on data flow and machine learning engineering for making predictions which can generate value for the client. You can watch the slides presentation here
Data engineering. You can see specific documentation here and the slides presentation here
Data analysis and machine learning. You can see the full technical documentation here You can see the online dashboard here
Fine tuning of bugs and deployment. You can see the full technical documentation here
You can see the full demo from the project here
You can see the full documentation from the project here