Learner Reviews & Feedback for Data Mining Project by University of Illinois at Urbana-Champaign
About the Course
Note: You should complete all the other courses in this Specialization before beginning this course.
This six-week long Project course of the Data Mining Specialization will allow you to apply the learned algorithms and techniques for data mining from the previous courses in the Specialization, including Pattern Discovery, Clustering, Text Retrieval, Text Mining, and Visualization, to solve interesting real-world data mining challenges. Specifically, you will work on a restaurant review data set from Yelp and use all the knowledge and skills you’ve learned from the previous courses to mine this data set to discover interesting and useful knowledge. The design of the Project emphasizes: 1) simulating the workflow of a data miner in a real job setting; 2) integrating different mining techniques covered in multiple individual courses; 3) experimenting with different ways to solve a problem to deepen your understanding of techniques; and 4) allowing you to propose and explore your own ideas creatively.
The goal of the Project is to analyze and mine a large Yelp review data set to discover useful knowledge to help people make decisions in dining. The project will include the following outputs:
1. Opinion visualization: explore and visualize the review content to understand what people have said in those reviews.
2. Cuisine map construction: mine the data set to understand the landscape of different types of cuisines and their similarities.
3. Discovery of popular dishes for a cuisine: mine the data set to discover the common/popular dishes of a particular cuisine.
4. Recommendation of restaurants to help people decide where to dine: mine the data set to rank restaurants for a specific dish and predict the hygiene condition of a restaurant.
From the perspective of users, a cuisine map can help them understand what cuisines are there and see the big picture of all kinds of cuisines and their relations. Once they decide what cuisine to try, they would be interested in knowing what the popular dishes of that cuisine are and decide what dishes to have. Finally, they will need to choose a restaurant. Thus, recommending restaurants based on a particular dish would be useful. Moreover, predicting the hygiene condition of a restaurant would also be helpful.
By working on these tasks, you will gain experience with a typical workflow in data mining that includes data preprocessing, data exploration, data analysis, improvement of analysis methods, and presentation of results. You will have an opportunity to combine multiple algorithms from different courses to complete a relatively complicated mining task and experiment with different ways to solve a problem to understand the best way to solve it. We will suggest specific approaches, but you are highly encouraged to explore your own ideas since open exploration is, by design, a goal of the Project.
You are required to submit a brief report for each of the tasks for peer grading. A final consolidated report is also required, which will be peer-graded....
1 - 8 of 8 Reviews for Data Mining Project
By Gary C
Aug 30, 2017
Sloppy final project, missing submissions links. Sections are no longer consistent. Nobody from UIUC responds, and nobody has in what seems like a year. A useless course.
By GANG L
May 3, 2018
Course content is excellent, but lack of support.
By Devender B
Apr 25, 2020
Very inactive course and staff. Hard to complete the outdated and unmanaged and unaccomplishable lab/practices.
By Ivan A
Nov 16, 2017
The project help me to practice the whole specialization algorithms and techniques.
By Rodrigo C C
May 24, 2017
Very good course!
By Hernán C V
Feb 3, 2018
By Logan V
Feb 11, 2021
Good ideas but code somewhat out of date and irresponsive people in charge. Also, the peer review system seems messed up, one of those I reviewed in 2021 had 2015 in its title which makes me think everything isn't being filtered properly.
By Aakriti N
Feb 12, 2021
I'm not really able to follow up with this course. I tried following all steps given but there were too many errors.