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.
- “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]
Games:
- “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]