sejong Univ. 2019.Fall DataAnalysisAndPractice
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Updated
Dec 20, 2019 - Python
sejong Univ. 2019.Fall DataAnalysisAndPractice
Covid19 analysis / Recommendation Systems
Springboard Foundations of Data Science - Capstone Project Repository
A standalone Java application, which runs queries on the huge Data Set of YELP and extracts useful information. Filtering is based upon Main Categories, Sub Categories and Attributes a business belongs to. Allows user to filter the business results again based on City, State, Zip Code, Days of week and timings. Application is build by extracting…
Big Data Mining using Apache Spark
End to end example how to read big (well, comparably) data from Kafka and write it down into Cassandra using Spark Structured Streaming. Using yelp dataset for illustration purposes.
Designed a system that will use existing yelp data to provide insightful analysis and to assist existing business owners, future business owners to make important decisions about a new business or business expansion.
Work done as a part of the Yelp dataset challenge.
Making individualized Yelp review recommendations using a network-based approach
Contains the GSQL scripts and TigerGraph solution to import and model the Yelp challenge dataset into TigerGraph respectively.
The goal of this project was to build an explicit recommender system using collaborative filtering for restaurants in Charlotte using Yelp's Open Dataset. I wanted to explore the mechanics of recommendations systems, and explore a new library in Surprise.
All projects or workbooks relating to my business analytics papers, programs and tools such as Python and SQL
Project for BANA 5040 Statistical Programming
Explores one method for building a content-based recommender system based on the public yelp dataset
A python script for Yelp customer profiling and business performance evaluation workflow
A yelp recommendation system based on user classification and review emotional analysis
Visualization of local business information included in the Yelp dataset (2017)
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