Added final tex file for missing parts. TODO: fill them

Author: Ttiki

main.tex

@@ -71,12 +71,21 @@ \section{Analysis}
 \subfile{sections/analysis}
 
 
-\section{Conceptual implementation}
-\label{sec:conceptualanalysis}
-\subfile{sections/conceptual_proposal}
+\section{Conceptual proposal}
+\label{sec:conprop}
+\subfile{sections/conprop}
+
+\section{Implementation}
+\label{sec:imp}
+\subfile{sections/imp}
+
+\section{Conclusion}
+\label{sec:conclusion}
+\subfile{sections/conclusion}
 
 \vspace{16pt}
 \section{Acknowledgment}
+\label{sec:aknow}
 I would like to thank all the faculty for their welcome, help, and support throughout this year. Helping me both on my paper and on my work at the lab. I would like to personally thanks Dr. Ernesto Exposito, Dr. Mamadou Lamine Gueye and Dr. Houssam Kansso as well as PhD. student Nicolas Evain for their help and welcome. A special thanks go to Dr. Mamadou Lamine Gueye for following me as a tutor for this last year of Master.
 
 I would also like to thank the entirety of the 2023-2024 SIGLIS and Industry 4.0 promotion with whom I've been able to share great times, knowledge and some fun moments during the year. A special thanks go to the SIGLIS promotion for supporting me for two year, especially my flatmates Dorian Cazabat, Lise Laville and Gabriel Das Neves Rodrigues. 

sections/analysis.tex

@@ -4,12 +4,15 @@
 We will now do an analysis from the literature review on how we can approach the problem, using what's been already made and how we can improve on it.
 
 \subsection{Factors}
+\label{subsec:analysis_factors}
 First of all, what differentiate this research from all the other we have read throughout the literature analysis is that we are not seeking prediction on student's dropout but rather on student success and early in the process and not during the curriculum year. However, there is plenty of interesting information we can gather from these papers. As described in the \ref{sec:soa} State of Art, we can gather factors that, in theory could help predict student's dropout. We can hypothesize that by using these factors to determine if one student is at risk of dropping-out, it could for another predict its success in a specific formation. From the list of factors we were able to gather, we have made a statistical analysis of the frequency they appear and their overall score within each paper they are mention it. Below, the table from this study concluding our research.
 
 \subsection{Machine Learning algorithm}
+\label{subsec:analysis_mlalgo}
 Secondly, we need to understand which algorithm model have been used the most and which present the best outcome for our need. As for the factors, we can extrapolate the problem and take it in reverse. So by learning which algorithm presents the best result to predict student's dropout, we could hypothesize that they could also be used to detect student's success. 
 
 \subsection{Analysis conclusion}
+\label{subsec:analysis_conclusion}
 Both our hypothesis and result must now be verified by providing a methodology and using a test dataset to send to our pipeline in order to feed our machines.
 We may find that one or both hypothesis are not correct and we will need to restudy factors and machine learning algorithm to answer our need and problematic. 
 In the next part, \ref{sec:conceptualanalysis} Conceptual implementation, we are going to present our methodology and workflow. Explaining the reasons for our choice of factors and algorithm as well as presenting our entire pipeline for our system.

sections/conclusion.tex

@@ -0,0 +1,6 @@
+\documentclass[../main.tex]{subfiles}
+\graphicspath{{\subfix{../res/}}}
+\begin{document}
+
+
+\end{document}

sections/conceptual_proposal.tex sections/conprop.texsections/conceptual_proposal.tex renamed to sections/conprop.tex

@@ -8,7 +8,7 @@
 But first, let's look into which data we have access to, and which we shall determine the pertinent data for ingestion into the system.
 
 \subsection{Feeding data}
-\label{subsec:conceptualimplementation_feedingdata}
+\label{subsec:conprop_feedingdata}
 Our literature survey has identified several key factors influencing student retention and success. We can extrapolate and hypothesis such wide factors could be used to determine student's success.
 These factors, hypothesized to be critical in predicting student trajectories, are:
 
@@ -45,8 +45,7 @@ \subsection{Feeding data}
 
 
 \subsection{Data workflow}
-\label{subsec:conceptualimplementation_dataworkflow}
-
+\label{subsec:concimp_dataworkflow}
 Our workflow, as depicted in Figure \ref{fig:dataworkflow}, is designed to systematically transform raw data into actionable insights. Even though we are looking to find excellence in registration for students, this model could be used and/or improved as a security measure to detect students at risk of dropping out.
 
 \begin{figure}
@@ -66,12 +65,29 @@ \subsection{Data workflow}
     \item Lasso Regression: To perform feature selection, enhancing model interoperability by isolating significant predictors.
 \end{enumerate}
 
+
 \subsection{Validation and Expected Outcomes}
-We anticipate that this workflow will yield a robust model capable of identifying excellent students. We will gauge the efficiency of our model through rigorous validation techniques such as \acrfull{roc}, \acrfull{pca}, etc. to ensure the reliability of our predictions. 
+\label{subsec:concimp_validexcpecoutcomes}
+ We anticipate that this workflow will yield a robust model capable of identifying excellent students. We will gauge the efficiency of our model through rigorous validation techniques such as \acrfull{roc}, \acrfull{pca}, etc. to ensure the reliability of our predictions. 
 To do this evaluation process, we will organize our datasets' intro training and testing sets. When training our model and test it for the first few times, we will feed it previous years' candidate profile and will examine the output of our model (i.e. potential excellence student) and compare the list given by it to the result of the entire class. This will help us determine which student could be classified as excellence and see if our model sends us back their profile or missed on some, or  to determine if our model as correctly found or missed on excellence.  
 We will test our models with different year to determine if this model could be implemented and use on \textit{the field} to help institution with the registration problem.
 
+
+\subsection{Tools used}
+\label{subsec:conimp_tools}
+To build this model using all these different algorithms, we are going to make usage of some tools as existing ones can help us build a mock up of the system without having the need to program and implement all these algorithm in code and build the workflow entirely in code. One such program is Rapid-Miner which is used to create such systems using a simple-to-use interface, dragging and dropping elements on a canvas and connecting each block to create our workflow. 
+By using such a tool, we are going to speedup the testing phase of our experiment and see if such a system could, in theory :
+\begin{enumerate}
+    \item Help detect excellence student in the masses
+    \item Efficiently be used in the field
+    \item Handle our data correctly
+    \item Be accurate enough to be used in the field (avoid too many false positive or negative)
+\end{enumerate}
+If our system proves efficient and accurate enough, we could then imagine building a bigger, more robust system, implemented directly with the information system used to retrieve student's inscription.
+
+
 \subsection{Usage on the field}
+\label{subsec:concimp_usagefield}
 We would like to explain our vision on how this could be implemented in universities and institution if the model proves to be viable and efficient. Such a system would pose an ethical problem and would need to be constantly measured, evaluated and should only be administrated by a third party without interest. It would be an impartial entity without any connection with the institution. 
 This model should not be used to exclude students, but should only point out the best result for each formation to reduce dropout rates throughout the country. However, other factors outside this model should be studied by administration for each institution for each formation to correctly select student not only based on the result indicated by our result but also taking into account these other factors. Moreover, any person using this model should not understand which information is used and how by the model itself. People selecting student's should not have anything to do with the registration data, the feeding process and the output of the model. To make this process as imperial as possible, this people should only receive the output without prior knowledge and the resource they usually get to chose student to do their selection.  
 Finally, as said earlier in this paper to protect students, no personal information and their identity (name, surname, country of origin, etc.) should not be involved in the process and the information shared should not allow anyone to trace back the student based on the information given.That is why having compartmentalisation is really important if this system should be put in place in any institution.

sections/imp.tex

@@ -0,0 +1,6 @@
+\documentclass[../main.tex]{subfiles}
+\graphicspath{{\subfix{../res/}}}
+\begin{document}
+
+
+\end{document}