Skip to content

Latest commit

 

History

History
46 lines (45 loc) · 4.84 KB

Project_List.md

File metadata and controls

46 lines (45 loc) · 4.84 KB
Raw

List of Projects (2017-18 M3)

  • AtomMe [x1]: The Prediction of Credit User's Overdue event Based on Machine Learning Method (Competition)
  • kylinanddragon [x1]: Individual P2P data of RenRenDai
  • ZheshengZhang [x1]: LSTM-based method for stock returns prediction
  • MattaUmaMaheswaraReddy [x2]: Housing Price (Kagggle)
  • diyawang [x4]: Factors affecting bank competitive power
  • JOY199603 [x1]: Forecasting the Price Change on IPO day
  • zhang-yunhe [x1]: Predicting default of credit cards clients (UCI)
  • Louie-Lin [x1]: Credit card default (UCI)
  • thomasraph [x1]: High crime area using cryptocurrencies
  • callmebyd [x4]: Lending Club Loan Data (Kaggle)
  • SunHao95 [x1]: Stock Index Prediction based on Wavelet Transformation and ARIMA-ML Model or TEA-ML Model
  • zsq96512 [x2]: FX rate prediction
  • Michaelpku [x1]: HCC Survival Data Set (UCI)
  • stuartphbs [x1]: Wine Quality (UCI)
  • yipanglin [x1]: NBA MVP prediction
  • DengQingqin [x1]: Sentiment Measures on Stock Market
  • LeiZHANG1995 [x4]: Bitcoin price prediction
  • zouqinjing [x1]: Breast tumor
  • xiechenpku [x1]: League of Legends matches (Kaggle)
  • 1994115 [x1]: League of Legends matches (Kaggle)
  • perfectyayra [x3]: Momentum TRADING WITH MACHINE LEARNING METHODOLOGIES
  • evanleungc [x4]: Predict low-risk profitable trading opportunity with high frequency trading data
  • labro [x2]: An investment model based on stock price info and stock revivews on the intenet
  • YedaDu [x4]: Idiosyncratic Market Value Factor: explaining market value by machine learning methods
  • UvoChow [x1]: Sentiment Analysis of Guba Forum
  • ansonwongphbs [x1]: TBA

Course Project Guideline

  • Report should be consist of one description .md and (possibly many) python notebooks .ipynb. ( .pdf, .ppt, .doc NOT accepted.)
  • In .md report,
    • 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 (example report from last year)
  • In .ipynb,
    • 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 committ codes, etc)
    • There should be no secret component (e.g., stock trading strategy)
    • Creativive (out-of-textbook) ideas are recommended for better result or result analysis
  • Deadline for updating report is 5.8 Tues Midnight (11:59 PM)