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            \hbox to 6.28in { {\bf EE 518: Machine Learning Theory \& Algorithms
            \hfill Spring \the\year} }
        \vspace{4mm}
        \hbox to 6.28in { {\Large \hfill Mini Project #1 \hfill} }
        \vspace{2mm}
        \hbox to 6.28in { \Large \hfill Due: #2, 11:59PM PT \hfill }
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        \hbox to 6.28in { {\it Student Name: #3} \hfill {\it Instructor Name: John Lipor}}
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\begin{document}

% PUT YOUR NAME BELOW WHERE I HAVE \X
\project{3}{June 7 (official) /June 14, \the\year}{\X}
% PUT YOUR NAME ABOVE WHERE I HAVE \X

\noindent \textbf{Note:} The ``official'' due data will be June 7, meaning I will not give feedback or answer questions after that date. However, you are welcome to take the extra week to continue working on your project, turning it in on June 14.

\section*{Introduction}

In this mini project, you will use machine learning to make some money (maybe\dots well, probably not) by predicting future stock prices \cite{kaggle2022japanese}. Future predictions of this sort are known as time-series prediction and are a common application of regression techniques. While we have not studied time series data, this problem can be easily cast as a supervised regression problem (see e.g., \cite{brownlee2016time}). This project involves predicting future stock
returns from the Tokyo Stock Exchange. However, the definition of a ``return'' is nuanced and includes not only return but the degree of uncertainty in this return. The competition also directly accounts for your ranking of stocks.

\vspace{1em}
\noindent \textbf{Notes:} 
\begin{enumerate}
    \item DSS rules apply for all mini projects.
    \item Be sure to cite any sources you used, including/especially Kaggle notebooks.
    \item You are free to use any algorithm(s) you choose, without exclusion.
    \item While our boosting method of choice has been \texttt{XGBoost}, you may also wish to consider reading about or using \texttt{LightGBM} or \texttt{CatBoost}.
    \item Getting your bearings in this project can be difficult. I recommend sorting the public code by \textit{Most Votes} for helpful insights.
\end{enumerate}

\section*{Requirements}

You must create a report in either \LaTeX or a Jupyter notebook that contains the following sections:

\begin{enumerate}
    \item Problem description 
        \begin{itemize}
            \item Explain what data you have to work with, what algorithms you will use, what your goal is, and why anyone should care.
        \end{itemize}
    \item Exploratory data analysis (EDA)
        \begin{itemize}
            \item There are many excellent tutorials on EDA online. Be sure to cite whichever ones you found helpful and comment on them in Sec. 6.
            \item Be sure to make any significant findings stand out and try to keep this concise.
        \end{itemize}
        \newpage
    \item Challenges
        \begin{itemize}
            \item What were the challenges you encountered when applying machine learning to this dataset?
            \item Did these challenges mainly result from the data? From results? Installing libraries to perform preprocessing?
        \end{itemize}
    \item Approach
        \begin{itemize}
            \item Provide a thorough but concise description of your approach, including (but not limited to) your approach to wrangling, preprocessing, and improving the performance of your predictor.
            \item This should be much clearer than what is typically found in Kaggle notebooks, but make sure it stays concise. A summary paragraph at the beginning of this section could be helpful.
            \item Since we have now formally studied model selection, you must use either a validation set or cross validation to tune your model parameters. Be sure to describe how you went about this and why.
        \end{itemize}
    \item Evaluation and summary
        \begin{itemize}
            \item \textbf{(NEW)} Be sure to carefully describe your understanding of the competition's evaluation metric and how you accounted for this.
            \item \textbf{(NEW)} In addition to your evaluation plots, you should make a late submission to the competition and report your score.
            \item Describe why/where your approach falls short in terms of estimation and approximation error. You may wish to generate a learning curve, as described by Ch. 11 of the text.
            \item Consider diving into the data to see if you can determine any trends regarding which points were misclassified.
            \item Likewise consider if there were any features that were particularly important or unimportant.
            \item Summarize your solution, describing what worked, what didn't, what the main limitations are, and any general conclusions about the dataset.
        \end{itemize}
    \item What I learned
        \begin{itemize}
            \item Describe the main skills/tools that you learned and used for this project and how you learned them.
        \end{itemize}
\end{enumerate}

\newpage
\section*{Grading}

The grading breakdown is below. Each section will be graded according to technical correctness, effort, and creativity. Note that clarity of writing is a major component. You should put yourself in the place of writing for a boss or senior in a workplace. If your writing is terrible, they will assume your work is terrible.
\begin{table}[h!]
\centering
\normalsize % The size of the table text can be changed depending on content. Remove if desired.
\begin{tabular}{ | c | c | }
\hline
\textbf{Item} & \textbf{Percentage} \\
\hline
Description & 5\% \\
\hline
EDA & 20\% \\
\hline
Challenges & 10\% \\
\hline
Approach & 30\% \\
\hline
Evaluation & 20\% \\
\hline
What I learned & 5\% \\
\hline
Clarity/conciseness of written communication & 10\% \\
\hline
\end{tabular}
\end{table}


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