MSBA Poster Session
Please join us for a Virtual Poster Session to highlight our MSBA Graduate students’ work.
MSBA Poster Session
Wednesday May 6, 2020
4:30 – 6:00 pm
For questions, email Ashley Mahanama (ashleahm@bu.edu).
Overview
The Virtual Poster Session provides an opportunity for MS in Business Analytics students to showcase their Capstone Project work. Participation in Capstone Projects allows students to complete analytics projects from start to finish, from formulating the business problem to finding a viable solution. Through these projects, students may employ their learnings from across courses, and gain experience in leading a project working closely with teammates. The final product includes deliverable creation (the poster) as well as presenting and communicating their work to others. MSBA students, faculty, and staff invite interested BU faculty, alumni, and industry professionals to these presentations to support graduating students and learn more about the MSBA program.
Increasing Meetup Profits
Session Title: Increasing Meetup Profits
Presenting Students: Zhen Li, Mandi Zhu, Youngjoon (Andy) Kim, Jingxuan Ma, Jiaying (Jannie) Hu, Hui (DD) Jiang
Session Description: We have built up a possible recommendation system for authenticated Meetup users based on their interests using the Meetup public dataset. Additionally, we have made casual suggestions on the Meetup website functions’ designs based on the A/B test. All of these components made were targeted to increase Meetup business profits.
Increasing Olist Profits
Session Title: Increasing Olist Profits
Presenting Students: Zhaoying (Jessie) Chen, Yiying (Jessica) Wang, Qifan (Shawn) Yang, Huiying (Ada) Ba, He Chen, Cheng Ting (Tenisa) Lee
Session Description: We analyzed one of the largest e-commerce platforms across Brazil, Olist, using tools such as R, SQL, Machine Learnings and Tableau to assist our study. Our goal is to generate key valuable insights from the dataset, focusing on e-commerce website performance, sales indicators, and customer buying behaviors. We will make recommendations based on our analysis to help Olist to thrive in its business.
Amazon Influencer Identification
Session Title: Amazon Influencer Identification
Presenting Students: Di Yao, Luke Towers, Ke (Sally) Zang, Yue (Luna) Wu, Shimiao (Fiona) Li
Session Description: Our capstone project aims to identify Amazon influencers from a large data set of 250 million reviews and product descriptions. We utilized machine learning to predict influencers based on a number of variables including the sentiment of review text and length of reviews along with five other variables. Following we estimated the revenue changed based on an influencers’ impact on a product.
Loan Repayment Capability
Session Title: Loan Repayment Capability
Presenting Students: Hang (Leo) Zhang, Xiaorui (Helen) Shen, Sizhe (CJ) Fan, Weifu Shi, Yinghui (Brenda) Wei
Session Description: The main purpose of our project is to help Home Credit Group better evaluate the paying capability of customers who have little or no credit history. We built a supervised machine learning model to predict whether customers will have difficulties repaying the loans. We then used unsupervised machine learning to cluster these customers into different groups.
Restaurant Industry Analytics
Session Title: Restaurant Industry Analytics
Presenting Students: Rui (Rachael) Xu, Xinman Liu, Melissa (Missy) Putur, Jiao (Jocelyn) Sun, Adil Wahab
Session Description: The incorporation of appropriate data-driven methods and the right mix of attributes can aid restaurants in understanding their impact on customers and what makes customers tick. This project looks to unpack the power of big data analytics in the restaurant industry by digging into restaurant attributes and reviews. What aspects are crucial for keeping a restaurant in business, and how do preferences of customers vary among different price levels?
Homeowner Insurance Models
Session Title: Homeowner Insurance Models
Presenting Students: Ziqin (Salina) Ma, Shihan Li, Qiaoling (Christy) Huang, Elmira (El) Ushirova, Chenran (Linda) Peng
Session Description: The purpose of our project was to improve on homeowners insurance models. We explored factors associated with risks and came up with new efficient variables and a precise model that can predict house damage losses at the zip code level in the USA.
Stock Performance Indicators
Session Title: Stock Performance Indicators
Presenting Students: Yanni Lan, Siqi Zhang, Yuyang Shu, Xiaoqi (Kiki) Hu, Chenhang Niu
Session Description: Our project focusing on investigating the relationship between financial indicators and stock market volatility. We hope to help investors to find out the most valuable indicators in predicting stock performance. In this project, we follow feature engineering logic to effectively selecting variables among 200 indicators and experiment in three different models.
Data Science Careers
Session Title: Data Science Careers
Presenting Students: Youming Qiu, Yue Gong, Yishuang (Sarah) Song, Minna Tang, Jingcheng Huang
Session Description: As MSBA students, we were keen to explore the data science job market in the U.S. The goal of this capstone project was to provide clear suggestions on what kind of skills and other requirements employers are looking for when hiring for data-related positions. At the same time we collected data on future career choices, the range of salaries and possible promotions that we can expect after graduation.
Flight Delay Insights
Session Title: Flight Delay Insights
Presenting Students: Huaiping Wang, Fucheng Yao, Limei (Mei) Huang, Kwangwoo (Ted) Kim, Eman Nagib
Session Description: We used analytical tools and machine learning methods on major U.S. domestic air carriers, to predict flight delays and derive valuable insights for better passenger flight experiences.
Mental Health Services Provision
Session Title: Mental Health Services Provision
Session Presenters: Alvaro Bernal, Andrey Lifar, Jiayin (Leighton) Li, Shangkun (Sherry) Zuo, Yuqi (Yoki) Li, Yue Ping
Session Description: We utilized survey data and a variety of machine learning models to (1) predict the value of mental health treatment for individual patients, and (2) determine any correlation between mental health and workplace provision of access to mental health services. Human resource departments would benefit from our findings and could utilize them to consider the potential value of providing access to mental health services.
Student Loan Defaults
Session Title: Student Loan Defaults
Session Presenters: Fernanda Lin, Mansi Tolia, Lyufan Pan, Kyle Blackburn, Hongyang (Patrick) Liu
Session Description: There are 45 million borrowers who collectively owe more than $1.5 trillion in student loan debt in the U.S. There is speculation about the effect of large scale default on the US Economy. Brookings Institute estimates 40% of loans may be defaulted on by 2023. The goal of our project is to determine the biggest contributors of students defaulting, so that we can help policymakers make more informed decisions and help students make better choices to reduce their chance at defaulting in the future.
Stock Market Analysis
Session Title: Stock Market Analysis
Presenting Students: Tyler McMurray, Senbo Zhang, Siyu (K) Liu, Qiuhao (Daniel) Chengyong, Zinan Chen
Session Description: The stock market is an important aspect of not only the U.S. economy but the world. This is an area full of information and data where there is also a lot of opportunities to benefit from. Our team is trying to merge alternative data with normal financial data to try to make better financial predictions.
Airbnb Price Prediction
Session Title: Airbnb Price Prediction
Presenting Students: Dekun Zhang, Yunlei Zhou, Yi (Vivian) Yu, Xiaoyang Xu, Zhengfang (Erick) Bao
Session Description:
Airbnb Analytics in Beijing & Boston
Session Title: Airbnb Analytics in Beijing & Boston
Presenting Students: Xiaohan (May) Mei, Ziyan Pei, Yuhong (Luke) Lu, Mengqing (Echo) Zhang, Peng (Jerry) Yuan, Jiayuan (Victoria) Zou
Session Description: Our business goal is to identify differences in Airbnb’s marketplaces and customers between Beijing in China and Boston in the United States. By generating supervised machine learning pricing modeling and conducting sentiment analysis in R, we aim to help Airbnb hosts to price their homestays better. By comparing the two cities, we also hope to enlighten the understandings of the Chinese Airbnb marketplace for the company.
Predicting Repayment Ability
Session Title: Predicting Repayment Ability
Presenting Students: Meiling (Maggie) Zhang, Dongzhe Shang, Yihan Jiang, Chengyu Liang, Kunpeng (Tom) Huang, Haolan (Helen) Ma
Session Description: Home Credit concerns about identifying the default risk of those specific groups of applicants and what’s the most appropriate finance plans that Home Credit could offer to those applicants. In our project, we aim to help Home Credit Group develop a model to make such predictions using different statistical and machine learning techniques. We also derive a formula that could transform the probability that the applicant will default into a credit score that scales from 300 to 850, which could provide Home Credit with a more flexible loaning option of what interest rate to charge the applicant based on their credit score.
Traffic Accident Prevention
Session Title: Traffic Accident Prevention
Presenting Students: Jinyan (Jimmy) Yu, Yanyue Fu, Aparna Raman, Kai (Kyle) Sun, Danting (Tiffany) Huang
Session Description: Our project seeks to address the problem of road safety by mapping the most common characteristics of crashes and road fatalities. We set the stage for the car-sharing industry in the present, and future year, to make rides safer for everyone and thereby increase their consumer base.
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