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# System prepended metadata

title: Project
tags: ['teach:MF']

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title: Project
tags: teach:MF
---



#  202109 機器學習與金融科技期末專案報告

請同學詳閱[說明文件](https://hackmd.io/RyNu7V3nToaT6Qb2WQujjw?both):目標，格式，以及資料來源。讓我們發揮同行致遠的精神，讓今天的自己比昨天的自己更好! 
- 可以個人做project。
- 也可以團隊project，每組最多3人。
    - 預設全組每位同學貢獻，所以同樣成績。
    - 口頭報告以一個project為單位。

## A project list

- :star: 班代協助確認課程事務是否如期規劃
- :apple: denotes if he/she has consulted with Prof. Teng. If I miss your apples, please inform me and I will correct it. 
- :melon: indicates a project that will be modified as a sample project for future course. 


| Index | Name| BackUp | Files Uploaded| Bonus|
|---|---|---|----|---|
|00|童秉儀|[Final]||　
| 01     | [李奕萱](https://hackmd.io/@erika-lee1217/BkKSijRXK)| [Final](https://hackmd.io/09ijIxufTLi2S7spoL3CIQ) | | :apple:|
| 02     | [杜知翰](https://hackmd.io/DzxDjLWJQES1L5rTgGEpAg)            | [Final](https://hackmd.io/8jp0u91QTYOn5VLwWYxWXg) | |
| 03     | [吳念主](https://hackmd.io/@UoGDAvQGTKOsX47tonoryQ/BJgnjjC7F) (物理碩) | [Final](https://hackmd.io/mmU9B_8DTLmh1E0WGXORLQ)||
| 04     | [陳敬文](https://hackmd.io/@6Lf2KfC_RVW8b9cdz3zI6A/SJxtn507K) (機械碩)|[Final](https://hackmd.io/7-iK3QNRTE-yaGRIz5p4dg)| |
| 05     | [陳以衡](https://hackmd.io/@dpmDf1pyTiGWgt0D6wf0vQ/SJcioj07t) |[Final](https://hackmd.io/TUJpLxWqQ4iXMkuu0JJfww)||
| 06 | [何建緯](https://hackmd.io/@1VjYHUs1Tu6Uv4YAP9aB-w/Byy6sjAQt) (管科) |[Final](https://hackmd.io/RCnBCOOTRoCnoiL2R1jC3A)| 
| 07     | [盧逸凡](https://hackmd.io/@YI-FAN/HJXJ_h6fF)   |[Final](https://hackmd.io/EeEOC4YzRLi-LdnddbxmAA)||:melon:|
| 08    | [徐培修](https://hackmd.io/@BuckHsu/BJMsVjCQK) | [Final](https://hackmd.io/IXuOme-3RPOxVA5Sy4Imtg)||:star::melon:
| 09    | [許智超](https://hackmd.io/@witty27818a/S1VL2o07t) (數據所)  |[Final](https://hackmd.io/flSHSXUPRSmwy3OH8MDPDQ)|
| 10    | [李柏毅](https://hackmd.io/@x2LAQ7HQR8i8O7THsvn_DA/HkIgt50mY) |[Final](https://hackmd.io/4LCDOXHbTcehQVTp-EPmCw?both) ||:apple::apple:
| 11    | [王聖晴](https://hackmd.io/@9mY24h7eRkuRXfYJ2chYkQ/SyrS9oCmY)  | [Final](https://hackmd.io/CKMW8bs7R8ikcJs_6Ew-RA) ||:melon:
| 12    | [陳姸榛](https://hackmd.io/@AT99JQAdQtuMAbv_MM1l3g/rkfF69CmK) | [Final](https://hackmd.io/KiHyY-ByQI6ncAYYFenK2A)|
| 13    | [黃子軒](https://hackmd.io/Hi_ea2JTSK-bLjy7DOnfFg?view) |[Final](https://hackmd.io/7IIJykvDQzCtqQjp8BsnVw)| 
| 14    | [宋昱奇](https://hackmd.io/Bnb3BJmJStW6RqQTY58fow?view)|[Final](https://hackmd.io/fK5MiB8UQSW6aBzo4RWUKg)
| 15    | [杜翔新](https://hackmd.io/@XfanakqjQ_KN5wuNvrJe-A/BJ652oA7t) | Miss |
| 16    | [尤皇倫](https://hackmd.io/@XVCGNox-St26K0lNOoVo4Q/Byhhij0mY) | [Final](https://hackmd.io/xreYeG5nRVa_kKkmqLtq9A)  | |:apple:|
| 17    | [胡藝馨](https://hackmd.io/@YIHSINHU/H1VStsCXY) | [Final](https://hackmd.io/JVTMJ2-EQ-edXAP53GezuQ)|
| 18    | [黎國俊](https://hackmd.io/vJrtGV08S1GR5ejgViGAnw?view) | 　[Final](https://hackmd.io/Io6ZSO80QXeyYzOt_DrYBw) ||:melon:|
| 19    | [李彥璋](https://hackmd.io/@MZrHyxdqRfydBJLzIahm4A/HJ4ZnoCQt) (電機背景)| [Final](https://hackmd.io/qNs0Q2c7RaeBcjkZS07knQ)|
| 20 |[賴冠諭, 陳廷威, 宋忻祖](https://hackmd.io/mh_qDMc2R0ishDnDII6yiw?view)|[Final](https://hackmd.io/2vBaqwsxSg2T10EI7MzjeA)| |:apple: 玉山|
| 21    |[王怡茹, 官顥, 薛育鴻](https://hackmd.io/@osmSEd5USX-Sq8Qy6tUDNg/S1yhCw7dY) | [Final](https://hackmd.io/YOeeSf__Q_-Crvzqv0qujA)||:apple::apple::melon: 玉山|
|22|  [杜承遠, 郭家妤, 孫崇棨](https://hackmd.io/A7_33uLITjeMVZbnDRXq9w?view)  | [Final](https://hackmd.io/_WkNZ59vTCehQ45k8dEzNg) ||:apple::apple: 玉山|


## Talk 1 (9/27): Motivations

In this talk, please describe your motivations clear. I was happy that almost every student did this part great! 


## Talk 2 (11/22): EDA and Proposal 

### EDA 

1. Refresh your motivations: Update title and keywords
2. Data visualization (EDA)
    - Feature descriptions: Provide explanations of each feature
    - Data visualization: Need to provide title and descriptions for each figure or table.
    - 在你們的HackMD精選有意義的結果。把剩下的繁瑣的細節，當作補充資料，提供link，放在Appendix。


### Proposal

這裡，同學必須清楚地指出:
1. Benchmark method
2. Measures for comparison 

### 1. Benchmark method
Simply speaking, model builing is to find a function $f$ to connect the explantory variable $x$ to the response variable $y$:
$$y = f(x,\theta),$$
where $x$ is the set of explanatory variables, and $f$ is the form of the model. So, briefly speaking, you need to decide:
1. which $x$ to use?
2. which $f$ to use? 

In your project,  you can compare which feature engineering outperforms the others? In this cases: 
- benchmark: features 沒有做任何轉換
- features 是否有做log-transformation, 
- featuers 是否做了PCA
然後你比較，以上三種情況，結果如何。

Or, you are focusing on supervised learning, the benchmark models would be possibly one of the following: 
- MLR: multiple linear regression
- LR: logistic regression
- [Multinomial Logistic Regression With Python](https://machinelearningmastery.com/multinomial-logistic-regression-with-python/)


### 2. Measures for comparisons? 

在以上兩種情境時，必須要說明清楚，你的benchmark model為何，並且如何利用Machine learning裡的方法，精進你的模型，以系統性的精進並且比較模型。
- 必須清楚說明比較的measures是什麼，並且列出各個模型做出來的數值結果。符號請用latex語言直接在HackMD上面寫。請不要貼snapshots，為這是不專業的!如果是做binary classification，比較的measures，常見的有Accuracy, precision, recall, F-1 score, AUC, etc. 如果是做trading or portfolio management, 常見的有: annualized return, standard deviationl, Sharp ratio, maximum drawdown, etc. 究竟是使用什麼measures，是和domain knowledge 息息相關。
- 需要具體說明有沒有做train-test split? in-sample versus out-of-sample? 比如在 binary classification，我們需要做80-20 train-test split，而在做交易，資產配置等問題，我們會做rolling-window appraoch. 究竟如何做out-of-sample comparisions，仍舊是和domain knowledge 息息相關。


## Talk 3 (12/27): Final presentations

Machine learning (i.e., supervised learning) provides a wide range of flexible and non-linear models. In your final presentations, you have to explicitly compare if you use machine learning methods to beat the benchmark method. 


<!---

| Index | Name|Comments (12/13) | keywords  |
|---|---|---|----|
| 01     | [李奕萱](https://hackmd.io/@erika-lee1217/BkKSijRXK)| benchmark? measures? | credit default risk, binary classification, logistic regression                             |      |     |
| 02     | [杜知翰](https://hackmd.io/DzxDjLWJQES1L5rTgGEpAg)            |  EDA? You should summarize results in HackMD (adding a colab link is not okay!)| Stock Prediction、Machine Learning Portfolio、LSTM Model                                    |      |     |
| 05     | [吳念主](https://hackmd.io/@UoGDAvQGTKOsX47tonoryQ/BJgnjjC7F)  | Benchmark? (1/N portfolio) measures for performance? | 南海泡沫事件, 股價預測         |
| 06     | [陳敬文](https://hackmd.io/@6Lf2KfC_RVW8b9cdz3zI6A/SJxtn507K) | benchmark? measures? | Stock price forecast, Predictive model, Explainable model, covid-19                         |      |
| 07     | [陳以衡](https://hackmd.io/@dpmDf1pyTiGWgt0D6wf0vQ/SJcioj07t) |Benchmark model: Multiple linear regression. ML: neural network. :question:| Stock price prediction model,Tensorflow,keras,Pytorch                                       |      |     |
| 08 | [何建緯](https://hackmd.io/@1VjYHUs1Tu6Uv4YAP9aB-w/Byy6sjAQt) | benchmark? measures?  | 主動選股, 股價預測  | 
| 09     | [盧逸凡](https://hackmd.io/@YI-FAN/HJXJ_h6fF)|  benchmark? measures? | Customer Transaction, lightgbm                                                              |      |     |
|10    | [徐培修](https://hackmd.io/@BuckHsu/BJMsVjCQK)  | benchmark? measures?  | 證券交易、股價、籌碼、券商分點 |
| 11    | [許智超](https://hackmd.io/@witty27818a/S1VL2o07t) |     Almost done (Do not use "spoiler"! it's difficult for the audience to read. ) | fraud detection, binary classification, audit risk    |
| 12    | [李柏毅](https://hackmd.io/@x2LAQ7HQR8i8O7THsvn_DA/HkIgt50mY)| Need to provide a benchmark!  | ML/DL, Financial Market, Automated Trading  |
| 13    | [王聖晴](https://hackmd.io/@9mY24h7eRkuRXfYJ2chYkQ/SyrS9oCmY) | Need to provide a Logistic regression as a benchmark. | credit card fraud detection,binary classification                                           |      |     |
| 14    | [陳姸榛](https://hackmd.io/@AT99JQAdQtuMAbv_MM1l3g/rkfF69CmK) | But, what is stochastic Gradient Descent Regression Formula? |加密貨幣預測 trading, machine learning, cryptocurrency|
| 15    | [黃子軒](https://hackmd.io/Hi_ea2JTSK-bLjy7DOnfFg?view)   | benchmark? measures? | 小額貸款, 違約風險評估    | 
| 16    | [宋昱奇](https://hackmd.io/Bnb3BJmJStW6RqQTY58fow?view) | benchmark? measures?  | 酒店取消. LR property、Machine Learning                             |  
| 17    | [杜翔新](https://hackmd.io/@XfanakqjQ_KN5wuNvrJe-A/BJ652oA7t) | Many materials are missing. benchmark? measures? |
| 18    | [尤皇倫](https://hackmd.io/@XVCGNox-St26K0lNOoVo4Q/Byhhij0mY) | Benchmark method? | big data analysis, Machine picking, Financial risk management, return forecast              |      |     |
| 19    | [胡藝馨](https://hackmd.io/@YIHSINHU/H1VStsCXY) | Benchmark? (Benchmark model is not exactly the same as benchmark result. We need to identify benchmark model so that we can recognize how to improve models.) | IPO企業                       |
| 20    | [黎國俊](https://hackmd.io/vJrtGV08S1GR5ejgViGAnw?view) |  You are almost done (with an acaedmic paper flavor)! Merge Tables 3 and 4. | Financial safety, Investment Bank, Early detection, Capital adequacy, Overall operation     |      |     |
| 21    | [李彥璋](https://hackmd.io/@MZrHyxdqRfydBJLzIahm4A/HJ4ZnoCQt) | Don't paste snapshot of formulas, you should type formulas directly using latex. |Momentum portfolios for comparisons                             | machine learning, future data, slidewindow, degree of freedom                               |      |     |
| 22, 23, 24|[賴冠諭, 陳廷威, 宋忻祖](https://hackmd.io/mh_qDMc2R0ishDnDII6yiw?view)| benchmark? measures?  | 玉山人工智慧公開挑戰賽-第一組 | 
| 25, 26, 27|[王怡茹, 官顥, 薛育鴻](https://hackmd.io/@osmSEd5USX-Sq8Qy6tUDNg/S1yhCw7dY) | EDA? (Can you paste the import information in HackMD direclty, and leave less information as summplementry materials using a link?) Problem formulation? Benchmark model? You should write down the measures but not paste the snapshots.  |玉山人工智慧公開挑戰賽-第二組 benchmark? | 
|28, 29, 30 |[杜承遠, 郭家妤, 孫崇棨](https://hackmd.io/A7_33uLITjeMVZbnDRXq9w?view) |benchmark? measures? | 玉山人工智慧公開挑戰賽-第三組 |

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