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Course Project (2019-20 M3)

Team List (Presentation Order)

Group Date Title
01 Friday Detection of fraudulent credit card transactions
02 Tuesday Stock selection with Chinascope sentiment data
03 Friday Detecting fake job posting
04 Friday Predicting Bank Term Deposit Subscription
05 Tuesday Yield-curve inversin and recession
06 Tuesday Short-term market timing strategy based on boosting ML algos
07 Tuesday Predictig credit card users' repayment behavior
08 Friday Predict Stock Returns
09 Friday Predicting trends of stocks and their future prices
10 Friday O2O Coupon Consumption Prediction Based on Past Consumer Behavior
11 Tuesday Detection of Malicious Website's URL
12 Friday Consumer behavior prediction, based on Taobao data
13 Friday Cancel or not? Predictive Analysis for Hotel Booking Data
14 Friday A Recession Indicator Generated by Interest Rates

Team Formation (3. 39 Sun)

  • Form a group (up to 4 students) and select data set
  • Designate a repository GITHUB_ID/PHBS_MLF_2019 of one team member for the team project.
  • Let TA know the repository to be used for th eproject
  • Put team members' student # and github ID in README.md (for the syntax of .md file, see markdown cheetsheet)
  • README.md will be eventually the report of your course project.

Data Selection (4.5 Sun)

  • No restriction on data set. However, business(fin/ma/econ) related data is welcome (extra credit for creative data selection and pre-processing)
  • Put the data under GITHUB_ID/PHBS_MLF_2019/data folder (if too big, put some samples)
  • Put a brief description of your data and the goal of the project in README.md (refer to markdown cheetsheet)

Project Guidline

  • Report should be consist of the summary in README.md and the execution in python notebooks .ipynb. ( .pdf, .ppt, .doc NOT accepted.)
  • In the README.md summary,
    • You may update your proposal file.
    • briefly describe your motivation, goal, data source, result and conclusion.
    • A few figure or table for summary is recommended.
    • Use links to data or .ipynb files (see past year examples below)
  • In the .ipynb execution,
    • Put command cell and edit cell (comments) in a balanced way. (Do not only put code!)
    • Put a brief table of contents with links (example: PML)
    • You may breakdown code into several .ipynb files by function (e.g., data cleaning, learning, result analysis). In that case, make sure to save intermediate result into file so that I can run the later steps (result analysis) without running previous steps (data cleaning, learning).
    • The use of .py file should be strictly restricted to function or class only. (Do not put any learning procedure in .py)
    • I should be able to reproduce the result from your code. Your code should run with no error. Code with error will be severely deduct your score. Make sure to run your code in a new session.
  • Other considerations:
    • Make sure the workload within team is balanced. (Add your team members to collaborators, each team members commit codes, etc)
    • There should be no secret component (e.g., stock trading strategy)
    • Creative (out-of-textbook) ideas are recommended for better result or result analysis
  • Deadline for updating report is 4.26 Sunday Midnight (11:59 PM)