Final Blog Post#
This dialouge has given me memories and experiences that I will never forget. The feeling of being immersed in a particular country isn’t a common experience everyone gets to share. But even more unique was the learning experience I have gone through. This wave of new information, particularly for me with never programming in python before this dialogue, pushed me and inspired me. I enjoyed python, and I am looking forward to using it in the future, I am glad that this is my first experience with it. I also learned so much with git, I never used it before with teams, pulling and pushing were new to me. I remember the first day in class Dr. Gerber asked everyone who had programmed in python before to raise their hands, it was everyone but me, and that was fine. Dr. Gerber, once again, thank you for making me feel okay with not knowing things, and then pushing me to learn. Dr. Fontenot, thank you for all of your advice and help on this project, you pushed us in a short amount of time which made us give you our best work. I loved the classes, and I loved this program.
This summer, I also want to work a little more on this project and make it refined. It would also be helpful if I looked at some SQL and the database architecture of our project before I start 3200 in the fall. I also need to make my code more professional because I did become somewhat unorganized during the project. I want to keep improving my skills and get prepared for the fall. This trip has been truly motivating, especially being surrounded by so many talented people.
I had many favorite moments that the list could go on forever. There are a few that were super unique and we share them with you. I loved the hike we did in Southern Luxembourg; the fields and views were something I will never forget. The pastry class was also fantastic, the treats were delicious, and everyone had a great time. The most underrated thing I think we did, that I haven’t heard much talk about, would be the computer museum. Although it looked interesting from the outside, learning so much history about computers and seeing them was fascinating.
For the final phase of this project, there were a few main things I worked on. I finally got the LSTM to work, after many attempts. It originally was never working on my computer, and then I sent my final to Dylan, and he ran it, and it worked. I then went on to debug my computer and find out the issue of why it never would run on my computer. I eventually created a new environment to run the file on and then it worked well. I then went on to make that a python file, where it could be implemented into stream lit, but I could not attempt implementing that because we did not have the stock data in a database. In the meantime, I worked on brainstorming more features for the website. Josh and I thought it was a good idea to add legislation data for the journalist persona, so I went web scraping. I spent a large amount of time scraping two websites that had embedded JavaScript, so I couldn’t grab any data. I downloaded a csv file, cleaned the data in it, and saved it into a new csv that could be implemented into a database. As I waited for the linear regression to be implemented into the website, I made the elevator pitch and practiced it while I waited so I could try to implement my LSTM into streamlit. I also started the slides and the Read.me while our group was having trouble with the databases and streamlit. I also started practicing my script for the elevator pitch while trying to help implement my LSTM model. Luckily we got the model into streamlit with some time left, but it somehow got messed up.
Resume Points - Public Interest Application Development:#
- Collected data by scraping and using APIs, then cleaned the data to be used for creation of tables, meaningful graphics, portfolios, and ML models.
- Created an LSTM stock prediction model using tensorflow, which accurately predicts any stock price in a short time period from our company database. The model only uses close price and the index of dates.
- Used the streamlit framework to build an interactive and useful portfolio tool that shows insights of stocks, politicans, and bills. Helped implement the ML models into the app.