Working on projects

Forgive the poor naming convention, this is the first time I'm seriously making a blog about what I'm doing. I've done this once before in high school, but like most high schoolers, I tended to add a lot of fluff to it.

I'm making this website for several reasons.

1. To start making benchmarks for myself

2. To showcase the work that I've been doing

3. To create a place where I can go back to projects that I've hit a roadblock on due to a lack of technical skills

I plan on making daily blog posts where I summarize what I've learned that day and to verbalize potential projects that I could pursue with that added knowledge. I also want to make a weekly post where I look back on the work that I've done and create goals for the next week. These posts will also include a report on whether or not I've reached those goals.

This past week, I wanted to learn more about machine learning and SAS. To learn more about machine learning and how to make these models, I used Kaggle to learn more basic Python and to start making mini models to give myself. For SAS, I used the learning resources that are given for free by SAS.

On machine learning, I feel that I haven't accomplished much. I learned more about Python, which was beneficial. In the past, I've only used Python to create statistical models which only requires knowing how to create formulas in Python, so learning more about the other uses of Python were quite nice. Learning these basics will help me clean data in a more efficient way. I wasn't able to really do anything new in terms of machine learning. I learned about decision trees and how to employ them in Python, but that was the extent of what I've done. In the past, I was able to create an LSTM to predict stock prices by using ChatGPT and code given by friends. I'm quite eager to get to a level where I can make these models without generative AI.

On SAS, I've made quite a bit of progress in learning the language. I've learned how to set data more efficiently and am more competent in creating libraries and using proc commands. A source of frustration was the lack of outside data sources as I tried numerous amount of times to use outside data sources and to clean it using SAS, but today I finally learned how to upload files for use by SAS. A large reason why it took me so long was that I was setting the data using paths that would come from my computer when SAS utilizes UNIX.

 This next week will require more practice in making machine learning models. I will continue to use Kaggle, but also will read more from "Machine Learning in Python® Machine Learning in Python" by Micheal Bowles. I would like to get past the second and third chapter and will put a lot of time in cleaning data sets in Python.

To learn more SAS, this next week I will continue to learn from the SAS website, but also will clean more outside data. I will also start making more data reports that give summary statistics with SAS.

I will also continue my project to see the policy effects that House Bill 3 had on school and school performance. This was a project that I had started with other classmates for ECON 460 but we were unable to finish due to a change in project in the second half of the semester. Currently, I have a general understanding of how the bill works, but want to expand more on how it affected schools and learn more about how income affects performance for jobs. I also want to learn more about standardized tests and the differences between each state as it was recommended that this project use Differences in Differences. Differences in Differences is rendered useless if the two comparison groups would have different outcomes without regard to the treatment variable. This project will be done in R.

This week I also learned more basic statistics, learning about sample spaces, events, and outcomes. I used a textbook called "A Modern Introduction to Probability and Statistics" by F.M. Dekking, C. Kraakamp, H.P. Lopuhaa, and L.E. Meester. I finished the second chapter this week, and want to have finished the third and fourth chapter, with both chapter's problems also being completed by Sunday of this next week.

For the summer, I also want to learn more about economic modeling, and I will learn using three textbooks that I found online. Those textbooks are "Trade Theory in Computable General Equilibrium Models" by Peter B. Dixon, Michael Jerie Maureen, and T. Rimmer; "Designing Public Policies An Approach Based on Multi-Criteria Analysis and Computable General Equilibrium Modeling" by Francisco J. Andre, l M. Alejandro Cardenete, and Carlos Romero; "Handbook of Computable General Equilibrium Modeling" edited by Peter B. Dixon and Dale W. Jorgenson. I don't expect to finish these textbooks, but I want to create a foundation for myself so that I can learn more about economic modeling at a faster rate later.

I expect to have a better structure for blog posts this next week. Tomorrow, my goals are to learn more statistics from "A Modern Introduction to Probability and Statistics", to read the second chapter of "Machine Learning in Python® Machine Learning in Python" and to continue to use Kaggle to learn about machine learning models, and learn more about standardized tests.