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Bao Yang

AM045 2017 Student Projects

12 Jul 2017

In this semester (2017 Spring), there are totally 12 team projects for my course “AM045: Introduction to Business Computing”. Although most students are only first-year undergraduates with no/little proir programming experience, they have done amazing jobs. Below, I list all projects (excluding one) which are categorized by topics.

Multimedia Data Analysis:

  • “Find the Member” by Pablo Garcia Arribas, Soohyun Lee, Begaiym Omurkanova, Hannah Rowe, and Dastan Yusupov.
    • This project uses the machine learning and computer vision techniques to detect faces of group memebers (image and video data) in real-time (using webcam).
    • [report] [slide]
  • “Classification of Voice” by 丁宁, 陈梦霞, 梁海娜, 王清凝 and 王敏.
    • This project uses the machine learning technique to classify the human voice (audio data) as either male or female.
    • [report] [slide]

Financial Analysis:

  • “Tone of MD&A and Its Impact on Firm Achievements” by 尤姝婉, 黄海昀, 齐月, 赵依婷, and 朱星宇.
    • This project analyzes the sentiment of firms’ MD&A disclosure in 10Q and 10K forms using text mining, and then predicts the firms’ future performance using machine learning.
    • [report] [slide]
  • “The Time-Varying Process of Beta Coefficient” by 陈琳云, 冯丽卿, 顾羽非, 陆怡然, and 殷铭.
    • This project calculates the Beta coefficient (a measure of systematic risk) and finds that the Beta coefficient changes over time by using Python plotting tool.
    • [report] [slide]
  • “Corporate Investment Decision-Making” by 陆之东, 乐雅馨, 冯圣飞, 杭慧丽, and 王莹.
    • This project uses the machine learning technique to predict the companies’ financial risk based on the F early warning model.
    • [report] [slide]
  • “Using Machine Learning to Predict Expected Return of Alibaba”, by 曲星凝, 李钰, 陳沛瑩, 孔婷钰, and 黄雅丽.
    • This project attempts to predict the expected return of the stock “Alibaba” using CAPM model.
    • [report] [slide]

House Price Prediction:

  • “Property Tycoon” by 马志宇, 刘润东, 潘乔, 吴铮, and 汪啸麟.
    • This project crawls the house price data from Lianjia.com using several Python libraries (e.g., Requests and BeautifulSoup), and then analyzes the factors that might affect the house price.
    • [report] [slide]
  • “Using Python to Predict the Housing Price” by 陈文帅, 于鹏, 叶恺, 罗绍博, 王程灝, and 茅宇润.
    • This project uses several machine learning algorithms to predict the house price.
    • [report] [slide]

Human Resources Analytics:

  • “IBM Performance Analysis” by 郑慧琳, 邓迪, 夏欣羽, 张婧文, and 李玥沁.
    • This project first uses the visualization technique to explore the factors that may affact employees’ job satisfaction, and then uses the machine learning technique to predict employees’ job performance.
    • [report] [slide]


  • “Pac-Man” by 薛承昊, 傅旻昊, 李旻镐, 黄鸿贤, and 丁润宇.
    • This project creates a classical game called “Pac-Man” (吃豆人) using Python.
    • [report] [slide]
  • “Guess IT” by Chia Jun Jie, Chong Ka Shing Claris, Igor Ivankin, and Shane Bradley.
    • This project creates a puzzle game called “Guess it” using Python.
    • [report] [slide]