CISC-480-Portfolio

Throughout my time at University of Saint Thomas, I have worked on many fun and interesting projects.

Some of my favorite projects can be found under the "Projects" page on my Personal Website I made in my CISC 375, Web Development course.

Project 1: CISC 440 AI and Robotics Coding a Lego Mindstorm Robot:

Background:

Our task was to build a Lego Mindstorm robot and edit a Python script that would cause the robot to be able to draw certain letters and short words.

Python Script Code:

https://github.com/nfriesen1/CISC-480-Portfolio/blob/main/Project%201%20Files/Project%201.py

Setup:

1. Set up the Lego Mindstorm Robot in "draw mode" giving it wheels and a set of rubberbands to hold a writing utensil
2. While connected to the Lego Mindstorm Robot, download and run the above Python script. This will direct the robot to draw letters (currently will draw M -> I -> L -> E)

Project 2: STAT 400 Data Mining and Machine Learning Trying to Predict Stock Prices Using Neural Networks

Background:

For our final project, two other students and tried to find a way to predict stock prices by using a neural network. We used stock price data from the last 5 years from top tech companies. These includeded Meta, Google, Amazon, Microsoft and Apple. We cleaned this data using Python and fed it into a couple different neural network models and tried to see if they were able to predict stock price changes accurately.

Python Script Code:

https://github.com/nfriesen1/CISC-480-Portfolio/blob/main/Project%202%20Files/StockPriceUpOrDown.py

Setup:

1. Download the stock price csv files here: https://github.com/nfriesen1/CISC-480-Portfolio/tree/main/Project%202%20Files/Stock%20Data
2. Download the Python Script Code above and run it on each on of the downloaded csv files to clean the data. Your data should end up looking like our Final Data: https://github.com/nfriesen1/CISC-480-Portfolio/blob/main/Project%202%20Files/Final%20Dataset.xlsx
3. Download JMP Statisical Software: https://www.jmp.com/en_us/home.html, and upload the finalized data.
4. Run a neural network on the data by going to the top menu bar and clicking Analyze -> Predicitive Modeling -> Neural.
5. Change the settings to your liking and click ok. This will create a neural network result page like this: https://github.com/nfriesen1/CISC-480-Portfolio/blob/main/Project%202%20Files/Final_Data%20-%20Neural.jrp

If you would like to learn more about this, you can refer to our final project paper here: https://github.com/nfriesen1/CISC-480-Portfolio/blob/main/Project%202%20Files/Final%20Project%20Paper%20Updated.docx

Project 3: STAT 360 Comp STAT and Data Analysis Final Project Path Diagram

Background:

For our final project, two other students and I looked at car crash data to see what factors lead to deadlier car crashes. With our findings, we creating a path diagram showing all these factors and their connections to lower/higher mortality rates in car crashes. We used R to clean and analyze our data.

Code: https://github.com/nfriesen1/CISC-480-Portfolio/tree/main/Project%203%20Files

Setup:

1. Download our cleaned data file here: https://github.com/nfriesen1/CISC-480-Portfolio/blob/main/Project%203%20Files/Project_Data.xlsx
2. Use our .RMD files and cleaned data to find useful information and connections about the data.
3. Use the info to create a large path diagram displaying connections between death rate in an accident and factors that lead to the accidents like this one: https://github.com/nfriesen1/CISC-480-Portfolio/blob/main/Project%203%20Files/Finished.drawio.png

If you would like to learn more about our project, you can refer to our powerpoint: https://github.com/nfriesen1/CISC-480-Portfolio/blob/main/Project%203%20Files/STAT%20360%20Group%20Project%20Presentation.pptx and abstract: https://github.com/nfriesen1/CISC-480-Portfolio/blob/main/Project%203%20Files/Abstract.pdf