Diego A Pérez Morales

Weekly Log

Week 1

• Attended orientation
• Attended data science workshop
• Attended good research practices talk
• Started looking for research papers having to do with clustering
• Started learning about what is clustering and what type of methods exist
• Started looking up information about K-Mean Clustering
• Had a meeting with Prof.Zimmer to brainstorm initial ideas for the research and establish what the research is going to be about
• Set personal goals and milestones for the summer

Week 2

• Attended the professional development meetings for the week
• Met with Prof.Zimmer to discuss the milestones we should tackle going into the future.
• Established recurring weekly meetings with Prof.Zimmer.
• Shared research data and other material through shared folder.
• Used the shared txt data to start preprocessing the data in python.
• Managed to create three functions in python to preprocess some sample text from the shared data.
• Coded my first experiment of the KMean model for the sample text data and managed to get my first graphs of the clustering.

Week 3

• Finished the RCR requirements.
• Met with Prof.Zimmer to show him my progress up until now and set some goals for the week.
• Commented and organized most of the code I've made up until now to make it look presentable.
• Met with past students who have worked on my research before and interchanged ideas.
• Made a script to group multiple text files into one text document, which can later be used on the previous algorithm I made.
• Added a feature to lemmatize the text of the file being fed to the algorithm.
• Made some minor changes to the preprocessing of the text as a whole.
• Shifted the focus of the research to be more of a thematic approach rather than looking for certain parts of a text.
• Started looking for supervised algorithms we can use to compare to the supervised one we have.
• Started preparing a research presentation of what I've done so far.

Week 4

• Attended profession meeting/presentation for the week
• Prepared a sample data file to test supervised M.L algorithms
• Managed to prepare a K Neighbors algorithm to run the sample data on.
• Managed to prepare a SVM (SVC) algorithm to run the sample data on.
• Got preliminary results with these supervised algorithms using the sample data.
• Kept preparing the mini presentation to show that I've done so far.

Week 5

• Finished my mini presentation
• Met a total of 4 times with Professor Zimmer to discuss the presentation and other topics regarding our research
• Presented my mini presentation in front of the group
• Developed a python script to convert docx documents into a unified text file containing all of the contents of the docx files
• Did some more cleaning on the newly unified data, specifically for the removal of single characters and empty strings
• Did some reading into some of the supervised ML algorithms I've used up until now, specifically SVC and KNeighbors

Week 6

• Attended the research poster presentation
• Received data representing the individual parts of each manually labeled document
• Reviewed the data I received in order to work with it
• Made a python script to process the 800+ docx documents and group them into a text file
• Made a python script to only process the docx documents related to a certain section of the manually labeled data
• Made a python script which categorizes the manually labeled data into a csv file, which can later be used to run on the supervised algorithms
• Met with Prof.Zimmer to update him on what I've been working on and talk a bit about the data he sent me

Week 7

• Attended industry panel
• Met with Prof.Zimmer to clarify some details about the data he gave and what we want to do with it
• Made a python script to have all of the data to process in one file
• Made a python script to further process the data Prof.Zimmer gave me, which corresponds to different parts of the data science programs
• Ran some tests using this processed data on the unsupervised clustering algorithm we have up until now
• Prepared the files of the data to be ran on the supervised clustering algorithm

Week 8

• Ran some initial tests, using the data from the different parts of the documents, on the supervised algorithms
• Met with Prof. Zimmer to discuss some results from the preprocessing and what we are going to do going towards the end of this REU
• Was able to run the data of the different parts with both types of algorithms and get some results
• Started writing down the results I got from my tests running the data for future reference
• Started setting up the layout for the research poster
• Went into a weekly meeting with the rest of the REU participants to see how everyone was doing with their work
• Met with Prof.Zimmer to show him some of the code that I did to get some results and my initial thoughts for the poster

Week 9

• Went ahead and finished the research poster
• Met with Prof.Zimmer to show him the poster and go over the results we got from the data
• Started working on a template for the research paper
• Did some research on on the SVC and KNeighbor algorithms in order to talk about them on the paper and poster
• Met with the rest of the REU participants to clear up any lingering questions about the end of the REU
• Finished the final formal presentation