Introduction
As a key component of the Predictive Modeling/Analytics course (MGSC 5125), students are
tasked with completing a project that not only demonstrates their understanding of the course
material but also challenges them to apply and expand upon these concepts in a practical context.
This project will constitute 40% of the final grade. The guidelines provided below are aimed to
guide the students through the project process.
Project Option:
Practical Predictive Modeling Project
Objective:
To demonstrate the ability to apply predictive modeling techniques to a real-world dataset and
extract meaningful insights, incorporating predictive, descriptive, and prescriptive analytics.:
Requirements:
Data Selection: Choose a dataset with more than 5,000 data points from reputable sources such
as the UCI Machine Learning Repository, Kaggle, or similar. The dataset should be relevant to the
theme of predictive analytics. No private datasets are allowed due to the delay in the legal process
of filing a nondisclosure agreement with the university. Here is a list of public repositories of data
that you can use for your projects. You need to select one of the following datasets or any other
public dataset that match one of the above themes that you selected. You can also use built in data
sets in the R software.
- https://archive.ics.uci.edu/
- https://www.data.gov/
- https://www.healthdata.gov/
- https://data.medicare.gov/data/hospital-compare
- http://www.dol.gov/open/data.htm
- www.toronto.ca/open
- https://www.ontario.ca/page/sharing-government-datahttps://nycopendata.socrata.com/
- http://www.gsa.gov/portal/content/181595
- http://open.canada.ca/en
- http://www.statcan.gc.ca/eng/rdc/data
- http://climate.weather.gc.ca/
- http://archive.ics.uci.edu/ml/
- http://githubarchive.org
2 - http://www.crowdflower.com/data-for-everyone
- http://www.kaggle.com/competitions
Analysis Methodology: Implement methods from each of the three analytics domains:
Predictive Analytics: Techniques like Logistic Regression, Random Forests, or Neural Networks
to forecast future trends or behaviors.
Descriptive Analytics: Use methods such as Data Visualization, Summary Statistics, or Clustering
to understand past and current trends.
Prescriptive Analytics: Apply Optimization, Simulation, or Decision Analysis to recommend
actions for optimal outcomes.
Tools for Analysis and Visualization: You may use R, Python for analysis, and for visualization,
tools such as Excel, Tableau, R, and Python are recommended.
Report Structure: Your report (15-20 pages) should include: - Introduction: Overview of the dataset and the chosen methods across the three analytics
domains. - Data Preparation: Describe preprocessing steps (e.g., cleaning, normalization).
- Method Application: Detailed explanation of how each method was applied within the
predictive, descriptive, and prescriptive frameworks. - Results & Insights: Present visualizations and interpretations of the outcomes.
- Conclusion: Summarize the key findings and their business implications.
- References: Cite all sources and tools utilized.
Additional Guidelines
Communication: Ensure open and regular communication within your group using digital tools
for coordination. Schedule regular meetings to discuss progress and upcoming tasks.
Individual Contributions: Assign roles that reflect each member’s strengths, ensuring active
participation in research, analysis, and presentation preparation.
Presentation: Prepare a coherent presentation with a clear introduction, objectives, methodology,
findings, and conclusion. Incorporate visuals and practice thoroughly to ensure smooth delivery.
Submission and Deadlines: Adhere to the project submission guidelines and deadlines to avoid
penalties.
Academic Integrity: Plagiarism will not be tolerated. Ensure all sources are accurately cited,
adhering to the university’s academic integrity policy.
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Conclusion
This project is a significant opportunity to act as a business analyst, leveraging data through
predictive, descriptive, and prescriptive analytics to uncover actionable insights. It represents a
chance to apply your analytical skills to complex data and demonstrate your capability to drive
meaningful business decisions.
Best of