Finance & Technology

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Customer acquisition and user experience are key metrics for startups, companies and financial institutions. Data science allows businesses to gain insights from user interactive data, such as unstructured texts from customer service chats and surveys. Two of the Data to Knowledge Lab’s Spring 2021 Capstone teams aim to help businesses gain these insights and ultimately improve customer experience.

Project #1 - Data Science Meet Emotion: Increasing Empathy in Customer Service Interactions Using Natural Language Processing

This project aims to build a model which predicts a real-time probability that a customer will have a positive experience during live customer support chats. To achieve that, five Rice students named the CS Chats team performed sentiment analysis and topic modeling with the data sets provided by D2K Affiliate member


Project #2 - The Positive Feedback Cycle: Using Customer Feedback to Understand User Pain Points.

The goal of the customer feedback project is to build data science tools to help the product management team at understand what types of users like and dislike the product.


Read the full story of these two projects >

Connecting the dots: How the entire financial world is connected

Team Members: Ye Chen, Seth Kimmel, Ankit Narasimhan, Jordan Pflum, Yifan Zhang

Building upon previous work done to model inter-relatedness of future contracts, we seek to understand market conditions under which such inter-relatedness occurs. This gives us insight into how the futures market operates and allows us to build powerful price prediction models using this knowledge.

Sponsor: Belvedere Trading LLC

Sponsor Mentor: Dr. Andrew Wendorff

D2K Fellow: Weilie Nie

Faculty Mentor: Dr. Dan Kowal

Project Title: Danceable Regression

Project Description: Our project looks at a Spotify datasets from 1921-2021 with features including danceability and acousticness. We explored several outliers in this dataset and used these Spotify-defined features to answer questions about song popularity and genre makeup.


Team Members:

• Natalie Goddard (Materials Science and Nanoengineering ‘21)

• Nathan McCoslin (Political Science, Asian Studies ‘22)

• Franco Gomez (Mathematical Economic Analysis ‘24)

• Andrei Mitrofan (Bioengineering ‘23)

• Hanna Gratch (Sociology ‘21)

Project Title: Airbnb: Host Classification and Price Prediction

Project Description: Our project seeks to optimize the experience of Airbnb hosts in order to allow them to price their listings most appropriately to maximize earnings. We have conducted data analysis of Austin, TX Airbnb listings in order to explore the differences in host classifications and the characteristics that most impact an Airbnb listing's price.


Team Members:

• Alana Pickens (Economics ‘23)

• Franklin Briones (Bioengineering, ‘21)

• Max Boekelmann (Sport Management ‘21)

• Daniel Cufino (Computer Science ‘24)

• Archit Chabbi (Bioengineering ‘24)



Cross-Community Comparison of Fire and Emergency Medical Services

Team Members: Toby Han, Sue Kim, Augi Liebster, Matthew Mutammara, Chris Yum

Our work aims to organize fire departments into cohorts based on demographic/incident factors via statistical clustering methods. This context will allow departments to compare how well they provide services to their communities and explore ways that similar departments have improved their operations and outcomes.

Sponsor: Intterra

Sponsor Mentor: Brian Collins, Molly Hausmann, Jason Posthuma, Amy Ehm

D2K Fellow: Daniel Bourgeois


Interested in working with Rice students and faculty on a real-world data science project? Send us an email at


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