Data Mining Project
Apply comprehensive data mining techniques to analyze Yelp restaurant reviews, from pattern discovery to recommendation systems.
Apply comprehensive data mining techniques to analyze Yelp restaurant reviews, from pattern discovery to recommendation systems.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Mining Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.5
(45 ratings)
7,403 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 3 more
Powered by
What you'll learn
Apply data mining techniques to real-world restaurant review data
Create visualization systems for opinion analysis
Develop cuisine mapping and dish recognition systems
Build restaurant recommendation algorithms
Integrate multiple data mining approaches for comprehensive analysis
Skills you'll gain
This course includes:
0.2 Hours PreRecorded video
6 peer-reviewed assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 7 modules in this course
This capstone project course focuses on analyzing a large Yelp restaurant review dataset using various data mining techniques. Students work on multiple tasks including opinion visualization, cuisine map construction, dish recognition, and restaurant recommendation systems. The project simulates real-world data mining workflows, integrating techniques from pattern discovery, clustering, text retrieval, and visualization. Through hands-on experience, participants learn to preprocess data, explore patterns, analyze content, and present meaningful insights for decision-making in the restaurant industry.
Orientation
Module 1 · 2 Hours to complete
Task 1 - Exploration of a Data Set
Module 2 · 1 Hours to complete
Task 2 - Cuisine Clustering and Map Construction
Module 3 · 1 Hours to complete
Task 3 - Dish Recognition
Module 4 · 1 Hours to complete
Task 4 & 5 - Popular Dishes and Restaurant Recommendation
Module 5 · 1 Hours to complete
Task 6
Module 6 · 1 Hours to complete
Final Report
Module 7 · 1 Hours to complete
Instructors
Pioneer in Information Retrieval and Text Mining Research
Dr. ChengXiang Zhai serves as Donald Biggar Willett Professor in Engineering at the University of Illinois Urbana-Champaign's Department of Computer Science, with joint appointments at the Institute for Genomic Biology, Department of Statistics, and School of Information Sciences. His groundbreaking research in information retrieval and text mining has earned him numerous prestigious honors, including the ACM SIGIR Gerard Salton Award (2021), ACM Fellowship (2017), and the Presidential Early Career Award for Scientists and Engineers (PECASE). After earning his initial degrees from Nanjing University and a PhD from Carnegie Mellon University, he has published over 200 papers with an H-index of 58, significantly advancing the fields of natural language processing, machine learning, and bioinformatics. His work has received multiple ACM SIGIR Test of Time Awards, reflecting his lasting impact on the field. Through his courses "Text Mining and Analytics," "Text Retrieval and Search Engines," and "Data Mining Project" on Coursera, he continues to shape the next generation of computer scientists while maintaining editorial roles with major journals and conference leadership positions in his field
Michael Aiken Chair
Jiawei Han is the Michael Aiken Chair Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign, where he leads the Data Mining Research Group. His research focuses on data mining, text mining, and intelligent systems, contributing significantly to the fields of machine learning and knowledge discovery. He has authored several influential books, including Machine Learning and Knowledge Discovery for Engineering Systems Health Management and Mining Software Specifications: Methodologies and Applications. Dr. Han's work is widely recognized, with numerous publications and citations in academic literature. He is actively involved in teaching and mentoring students in advanced computer science topics related to data mining and information systems.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.