This repository is a collection of all three portfolio projects completed in ENDG 233 in the Fall 2024 semester. Keep in mind that these projects have limited scopes, so they are not very advanced. Each of these projects earned 100% for exceeding expectations by using skills not covered in the scope of the course.
Project 1 is a small project designed to calculate information on racecars and their expected race time by taking into account multiple factors. The program considers variables such as speed, acceleration, and track conditions to estimate performance. While basic in scope, this project introduces fundamental programming concepts and numerical calculations in Python. Future improvements could include more advanced physics models and additional parameters for a more realistic simulation.
Project 2 is a slightly larger but still relatively basic project that allows users to view certain information on for-sale houses in Calgary. The program processes housing data and provides insights into price, location, and other relevant details. This project introduces data handling techniques, simple file I/O operations, and filtering functionalities to help users analyze real estate information efficiently. Additional functionality, such as integrating live real estate listings or calculating mortgage estimates, could be useful extensions.
Project 3 is a slightly more advanced project, but still basic regardless. This project allows users to view data on US Presidents, Consumer Price Index (CPI), and employment rates over the years. The program integrates multiple datasets and enables users to retrieve historical trends and statistics. Some of the key functionalities include:
- Searching for information on specific presidents.
- Analyzing CPI trends and inflation over time.
- Viewing employment rate fluctuations across different time periods.
This project expands on data processing techniques, structured data storage, and user-friendly data retrieval. While it is still within a limited scope, it demonstrates the ability to work with multiple sources of information and provide meaningful insights. Potential future enhancements could include interactive visualizations, predictive analytics, or expanding the dataset to include global economic trends.
Each of these projects serves as a foundation for further development and demonstrates an understanding of Python programming beyond the course requirements. Future improvements could include additional features, data visualizations, or more interactive elements to enhance usability. These projects highlight core problem-solving skills and an ability to work with real-world datasets, laying the groundwork for more advanced programming applications.