Data Mining

Milestone Due Dates

Abstract	Thursday, January 30, On Google Colab, Submit your URL to Piazza	2 pts
Progress Checks	March 03, March 31 On Google Colab	4 pts
Presentation/ Demo	April 17 (11.59pm) - submit your Video link to piazza forum	7 pts
Final Report	April 17 (11.59pm) On Google Colab	7 pts

Introduction

For the project, you will work in teams of either 1, or 2 students on a problem of your choosing that is interesting, significant, and relevant to Data Mining. The ultimate goal of your course project is to develop a new tool to tackle some interesting real-world problem around data. At the end of the semester (Week 14), we will hold a presentation during our regular class time for your project presentation/demonstration. All members of a group will receive the same grade on group work. Therefore, it is in your interest to choose other group members (ideally, first day of the class if possible) who have the same goal in the class as you do. It is also in your interest to work together and ensure that all tasks are completed effectively. Your scores on group work may be adjusted based on your contribution (peer-evaluation).

We are going to use Google Colab (Colaboratory) for your report (https://colab.research.google.com/), a free Jupyter notebook environment that requires no setup and runs entirely in the cloud. With Colaboratory you can write and execute code, save and share your analyses, and access powerful computing resources, all for free from your browser

Project Abstract

The abstract (in Googel Colab) should include the following information:

Data Source

URL
a short description

Problem statement
Project participants and email addresses
Work plan sketch (what tasks do you expect to do and in what order, how long do you expect them to take, and who will be responsible for the tasks).

● You can take as much space as you need for the project abstract, but I would guess that most would be something very short.

● You need to have an acceptable abstract submitted by the deadline.

Project Presentation (10 Minutes video)

Use Zoom (you have access to zoom pro via ODU https://www.odu.edu/ts/collaboration-tools/zoom) or any other video recording tool to record a 10 minute or less video of your project work and upload it to YouTube. You can show your implementation/demo (use the screenshare option) and also your presentation slides or Google Colab. Your presentation/Demo should succinctly tell us *why* we should care and *what* interesting insight you have about the chosen data mining project. Give us some insight into the tough / cool / interesting aspects of your project. This is your time to shine, so carefully prepare what exactly you want to show off that will impress us in this summary. View the audience as potential upper management in your company -- so convince us that your problem is important, that you have the appropriate insight about the dataset.

Follow the Guidelines preparing your Summary section for the talk (This should be at the very end of your Colab or very first slide of your video slides)

Have a title of the project and your name at the verfy first in your Summary Section of the Colab or have it in your very first slide.
Have a clear outcome presented (charts, graphs, key conclusions)

Know what you want your audience to take away from your presentation. Ideally, you would like the audience to leave with an understanding of what you’re doing and why you’re doing it.

Tell a Story

You may like to present your punch line first, tell it like a story, with a beginning, middle and an end. It’s not easy to condense your project into a short presentation, so you may find it easier to break your presentation down into smaller sections. Try writing an opener to catch the audience attention, then highlight your key findings, and finally have a summary to restate the importance of your work.

Engage your audience!
Submit your Presentation/Demo YouTube link to Piazza thread.

Project Final Report

A comprehensive report describing the project. This should be a "complete" document, so it should include front matter (title page, abstract, table of content, chapters), or a sidebar index that connect to your report elements. These should include problem statement, explain your design and implementation, results and evaluation. This report should stand by itself as the archival description of the project.

The is the continuation of your same Google Colab project document.
Colab file title should be "YourLastName_CS495/595_DataProject"
Your results and evaluation (or evaluation strategy)

What metrics used (or will you use) to evaluate the success of your project?

Performance measures (how you measure them)?
Other criteria?

You should address the same questions as those you have addressed in the previous reports (abstract, progress checks), only with more details, especially regarding some of the challenges that you need to solve and your experimental results if any.
You should also include your conclusions from the study and point out how your work can be further extended (i.e., future work).
References if available (this should be the very last section of your Colab)
Provide as much context as you can—any kind of diagrams and illustrations that will make it easier for us to understand and evaluate your effort. Graphics are always helpful, you have probably heard the saying “a picture is worth a thousand words”!

Do not just show the diagrams—for all figures, tables, charts, and diagrams provide some narrative discussion! Unfortunately, diagrams, particularly technical diagrams, are rarely if ever self-explanatory. You should document the alternative solutions that you considered as well as the arguments for the final choice. Diagrams only represent your final solution, but do not explain why you decided on this solutions and what alternatives were considered. Hence, all diagrams must be accompanied with explanation and discussion of alternatives and tradeoffs. Anything that could lead to ambiguity or misunderstanding on the reviewer’s part, should be clearly explained. Explanations should be written in prose and key arguments highlighted in bullet points.

There is no limit on the number of pages (or size) for the report. Of course, you should avoid stuffing your report with redundant or irrelevant material.

Project suggestions