OBA 410/510 Lundquist College of Business
Python Data Analytics
Term Project Proposal (Initial Presentation and Report)
Project Topic
To start, go over the examples for data analytics that I provided during the first class (see
below).
• Predict whether a person will have a heart attack
• Predict whether a patient will survive a surgery
• Predicting the success rate of a marketing campaign
• Detecting credit card fraud transactions
• Predicting the shopping behaviors of our customers
• Identifying the success factors for a football team
• Recommending which movies to watch
• Customize an email spam detection system.

This will give you some ideas about defining your project topic.

Attention: When you pick your topic/data, please enter your topic information in the shared
spreadsheet titled "Term Project Topics." The link is also available in the "Term Project"
module in Canvas.
- No more than two groups can work on the same topic/data. Therefore, before
finalizing your topic, check the spreadsheet to ensure your group is allowed to work on
the subject.
- Topics are first come, first serve! Thus, make sure to enter your topic in the
spreadsheet as soon as you can.


Acquiring the Data
 It is always best to get the data from a company you know or work for.
 If you do not know such companies, a couple of websites provide free/publicly available
datasets. Besides these, you can always search for other data sources online.

https://github.com/awesomedata/awesome-public-datasets#economics
https://www.data.gov/
https://www.kaggle.com/datasets
https://archive.ics.uci.edu/ml/datasets.php
https://cloud.google.com/bigquery/public-data/
https://registry.opendata.aws/
https://www.springboard.com/blog/free-public->
OBA 410/510 Lundquist College of Business
Python Data Analytics


Presentation
You need to present your proposal (in about 6 minutes) in the second class during week 6.

In your presentation, you should clearly define:
 The problem (starts with a brief background about the business problem followed by a
clear definition of the problem)
 The objectives (Your goals and objectives in the project)
 How you acquired the data (the source)
 Characteristics of your data, number of features, and number of records
 Brief description for features in data
 Your data should have at least a couple of thousand records (more than 2000 or so). It
also must have enough features to predict the target.
 You should explicitly specify your target variable in the dataset and determine whether
your problem is classification or regression.
 Provide the results of a preliminary model using k nearest neighbor algorithm

You need to have about 6-8 slides.
You will have 6 minutes to present
Your slides have to look professional


































































学霸联盟
































































































































学霸联盟