Data Mining in Engineering, IE 7275
Project Guidelines
IE 7275: Data Mining in Engineering
OBJECTIVE To demonstrate the applications of data mining principles and processes
on a practical problem. It counts for 10% of the final grade.
GROUP EFFORT Students work in groups of two on the project.
DESCRIPTION The project is intended to give students a hands-on experience of the entire
data analytics process, including business problem definition, solution
design, data selection and/or collection, data processing, data exploration,
data reduction, transformation, and variable selection, model building,
algorithm implementation, and predictive performance evaluation,
visualization and reporting. Students can choose a datasets of their own
choice or they can choose datasets from any of the sources listed below.
Data Source • Kaggle Datasets
https://www.kaggle.com/datasets
• UCI Machine Learning Repository
http://archive.ics.uci.edu/ml/index.php
• US Open Data Project
https://www.data.gov/
• Financial Data
https://www.quandl.com/
• Awesome Public Datasets
https://github.com/awesomedata/awesome-public-datasets
• Datasets Subreddit
https://www.reddit.com/r/datasets/
• Google BigQuery Public Datasets
https://cloud.google.com/bigquery/public-data/
• 100 plus free data sources
https://www.columnfivemedia.com/100-best-free-data-sources-
infographic
KEY
MILESTONES
• Due on January 24: Project proposal (see Proposal Guidelines)
• Due on Feb 14: Data collection and processing.
• Due on Mar 10: Data visualization, exploration and selection of data
mining models, and implementation of selected models.
• Due on Mar 27: Performance evaluation and interpretation.
• Due Apr 3: Complete project and submit a report with detailed
documentation of all the steps.
• Due Apr 10 A power point slide deck (see Presentation Guidelines).
• Project Presentations Apr 14/17 (see Presentation Guidelines).

Data Mining in Engineering, IE 7275
GRADING
POLICY
The project is designed to test your ability to apply your fundamental
understanding of the material to a practical problem unlike to a well-
structured homework problem.

Your interim reports serve the purpose of documents’ steady progress on
their projects. Interim reports are not graded. If students need feedback on
their projects at any point in the semester, they are encouraged to make an
appointment with the instructor or TA to discuss. The projects are
evaluated at the end of the semester based on the following criteria:

Project selection and problem definition 10%
Data collection 10%
Data exploration, visualization and processing 10%
Dimension reduction and variable selection 10%
Model exploration and selection 10%
Model performance evaluation 10%
Performance visualization 10%
Study progress through the semester 10%
Project presentation 10%
Report organization/writing/clarity 10%