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Data Mining for Business Intelligence

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Data Mining for Business Intelligence
Team Project Presentation and Project Report: Handout
BADM 557: Decision Support Systems

The goal of the term project is to develop a useful and viable prediction or classification model based on data. You will need to develop a research question, which you refine further based on the availability of data. You may need to merge multiple data sets together.
Process:
• Each team of 2 or 3 students will work on a business problem involving data analysis with real data. The project will focus on classification and prediction methods we covered during the semester.
• A presentation session is scheduled for the last week of class. You are encouraged to be creative in your presentations.

Choosing your project and data
You are free to choose any data set that is accessible by you. Data can be obtained through your work experiences, assuming that you have obtained the necessary clearance and authorization to use it. You may also use publicly available data; much of it can be downloaded from the Internet. The University of Illinois also gives students access to many useful databases. I encourage you to focus on a topic that relates to a personal or career-related interest. Ideally, your dataset should have at least a hundred data points (unless the whole dataset you decide to use from a standard place is smaller). Smaller datasets may cause interpretation problems.
The resources listed below are only to give you a sense of the kinds of opportunities that exist. You are free to use these ideas as a starting point in developing your project. If you are using a dataset that is similar to or already used in prior research, I would be looking for you to provide new insights beyond what has already been shown. I am looking for original work.

Project Data Possibilities

1. Research-quality datasets: https://bitly.com/bundles/hmason/1
a. It a great hub for many different datasets. Links to data sources in many other websites used by researchers in many different

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