#Academics
This repository contains a collection of my academic assignments, projects, and applied coding exercises developed throughout my coursework. It is intended to document my learning process, demonstrate my technical growth, and showcase my ability to apply theoretical concepts in practical and meaningful ways. The assignments included in this repository span multiple areas, including machine learning, data analysis, cybersecurity, and general programming. Many of the projects follow a full workflow—from problem definition and data preparation to model development, evaluation, and visualization—to reflect real-world application of concepts. This repository is not meant to represent production-ready code, but rather an evolving portfolio of my academic and technical development. Many projects include experimentation, iteration, and ongoing refinement as I continue to strengthen my skills.
Setup Instructions: Before running any code, ensure that you install all required dependencies by running the following command in your Python environment:
pip install -r requirements.txt
Important Notes:
- You must upload or include the requirements.txt file within your working environment or repository before running any scripts.
- Any required CSV datasets must also be uploaded into your working directory or correctly referenced in the code. Ensure file paths match your local or notebook environment.
- Some libraries, such as TensorFlow, may fail to install depending on your Python version.
Recommended Environment: Jupyter Notebook or JupyterLab is recommended for running and interacting with the assignments, as it allows for better visualization and step-by-step execution.
Purpose:
- Reinforce academic concepts through hands-on implementation
- Demonstrate technical and analytical capabilities
- Track progress and continuous learning
- Provide reusable references for future work
Final Note: This repository reflects my ongoing journey as a student and practitioner. Each assignment contributes to a deeper understanding of the field and supports continued growth in both technical and analytical areas.
– Taylor