Earticle

초록

영어

The paper attempts to document the application of relevant Machine Learning (ML) models on Yelp (a crowd-sourced local business review and social networking site) dataset to analyze, predict and recommend business. Strategically using two cloud platforms to minimize the effort and time required for this project. Seven machine learning algorithms in Azure ML of which four algorithms are implemented in Databricks Spark ML. The analyzed Yelp business dataset contained 70 business attributes for more than 350,000 registered business. Additionally, review tips and likes from 500,000 users have been processed for the project. A Recommendation Model is built to provide Yelp users with recommendations for business categories based on their previous business ratings, as well as the business ratings of other users. Classification Model is implemented to predict the popularity of the business as defining the popular business to have stars greater than 3 and unpopular business to have stars less than 3. Text Analysis model is developed by comparing two algorithms, uni-gram feature extraction and n-feature extraction in Azure ML studio and logistic regression model in Spark. Comparative conclusions have been made related to efficiency of Spark ML and Azure ML for these models.

목차

ABSTRACT
Ⅰ. Introduction
1.1. Related Works
1.2. Motivation and Goals
Ⅱ. Background
2.1. Microsoft Azure Machine Learning Studio
2.2. Databricks
2.3. Preliminary Work on the Machine Learning Models
Ⅲ. Methodology
3.1. Data Description
3.2. Hardware Specification
3.3. Machine Learning Workflow
3.4. Process in Azure
3.5. Process in Databricks
Ⅳ. Results and Discussions
4.1. Matchbox Recommender
4.2. Collaborative Filtering Recommender
4.3. Classification Models
4.4. K-Means Clustering
4.5. Text Analysis
Ⅴ. Conclusions and Future Work

저자

• Ruchi Singh [ Data Analyst, CISCO, USA ]
• Jongwook Woo [ Professor, IS, College of Business and Economics, California State University Los Angeles, USA ] Corresponding Author

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