本次韩国代写是一个Python机器学习的project，要求生成代码和PPT报告
You must follow the following directions:
Your project must be an empirical work
You must use actual (financial) data set
You don’t need to use full data set, but your data set must be enough to test your questions
Must use one of machine learning methods covered in the class
Simple Linear regression or Logistic regression is not included
You can use Regreesion as your benchmark to compare your chosen methods with basic regressions
Suppose you want to evaluate the credit scoring
Key variable is “Credit Score”
What’s the explanaroty variables : Gender,
Occupation, Age, Housing Type, Salary, Past Loan
History, Consumpition patterns, ….
But, we may not collect all the variables
Some sources are private
Missing
Not a long history (only 1–2 months data)
Before starting your project, you should check data availability
Some are already well defined and available (maybe not free) : stock price, company financials, etc.
You might collect the data manually : CEO information, …
Thus, you have to prepare appropriate data set after identifying your project
Popular data base (Not free, but SKKU subscribes)
CRSP : US stock price, trading volume
S&P Compustat : Company financial information of
North American Companies
In this project, I will not ask a full paper
Instead, you should submit a summary of your results as a ‘PPT’ format
The final output in your PPT file should include:
The final output in your PPT file should include:
Project overview (1 or 2 page) : Title, Problem of your project
Literature or Backgrounds (1 2 page) : Existing studies (Listing), Problems of existing studies
Your objective (1 page) : Based on problems in existing studies, how does your project contribute to the literature?
Data (1 page) : Data Sources, Sample Period, Observations, Main variables
Summary Statistics (1 page table): Mena, Std. Dev., Min, Max, Median of your variables in your data
Empirical Strategies (2 or 3 pages) : Machine Learning Methods used in your project
The final output in your PPT file should include:
Empirical Results (Within 5 pages) : Your main results
In these slides, you show your main empirical results
The performance evaluation must be included
Conclusion (1 page) : Summarize your result
