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.

yelp dataset challenge round 12 (NLP)

Analyzing Yelp user data using SVM to determine Yelp's Eliteness criteria

Implemented a model that is capable of predicting a restaurant rating taking into account several factors such as reviews and restaurant facilities. Analysis of review is done based on NLP techniques that include polarity analysis, TF-IDF which are all followed by pre-processing.

• Developed a Recommender System for restaurants by performing analysis on data preprocessed from Yelp Dataset. • Used Altering Least Squares method with Matrix Factorization and Neighborhood Model to train and build the Recommender System. • Tested the Recommender System with multiple rounds of Cross Validation technique and 16% prediction erro…

Restaurant Recommendation Systems based on the Yelp dataset (2019) using Ensemble method based on Images and text from reviews.

Self-defined data science project performed on the Yelp Open Dataset for the purpose of understanding underlying trends and biases on the platform.

This is a non-commercial research project using data provided by Yelp

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.