This repo exists to house archived projects from completed Bachelor's degree.
AllStarRosters | Decoding MLB All-Star Rosters - Exploring Player Performance and Selection Relationship
This project delves into the analysis of Major League Baseball (MLB) All-Star rosters. Utilizing Python and data visualization libraries, the project explores and visualizes the rosters over the years, identifying patterns and trends. The aim is to understand the relationship between player performance and All-Star selection, investigating whether performance metrics are predictive of roster selection. The project provides valuable insights into player selections and roster composition for different seasons.
CharityMLFindingDonors | Predicting Donors with ML - Optimizing Fundraising Efforts with Predictive Modeling
This project involves the development of a machine learning model to pinpoint potential donors for a fictitious charity organization, CharityML. Leveraging Python and a variety of machine learning algorithms, the project meticulously analyzes and preprocesses data, then trains the model to predict which individuals are likely to donate. The objective is to target individuals earning more than $50,000 annually, thereby expanding the potential donor base while minimizing the overhead cost of sending mail. The project encompasses data wrangling, feature engineering, and model evaluation to achieve precise predictions, offering a cost-effective solution for donor identification.
This project involves the development of a class roster management system using C++. The system is designed to efficiently manage student and class information, including enrollment, grades, and attendance. The aim of the project is to streamline class management processes for educational institutions, simplifying the tracking and management of student data.
This project presents a comprehensive analysis of Major League Baseball salaries from 1985 to 2016. Utilizing Python and data visualization libraries, the project explores potential relationships between player salaries and various performance metrics. The findings are presented in an interactive Tableau story, offering a visually engaging exploration of trends and patterns in the data.
IdCustomerSegments | ML-Based Customer Segmentation - Harnessing Unsupervised Learning for Core Customer Identification
This project applies unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. Leveraging Python and machine learning libraries, the project employs clustering algorithms to segment customers based on their purchase behavior and characteristics. The goal is to pinpoint different customer segments and comprehend their preferences, enabling the development of targeted marketing strategies for higher returns.
This project dives deep into the data from the FBI's National Instant Criminal Background Check System (NICS). Utilizing Python and advanced data analysis techniques, the project uncovers intricate patterns and trends in firearm purchases over time. The objective is to comprehend the underlying factors influencing firearm purchases, providing critical information for policy-making and law enforcement.
OpenStreetMapAustin | Austin OpenStreetMap Analysis - Cleaning, Analyzing, and Visualizing Austin's Geospatial Data
This project involves the meticulous cleaning and comprehensive analysis of OpenStreetMap data for the city of Austin, Texas. Using Python and data manipulation techniques, the project ensures data quality and consistency through thorough preprocessing and cleaning. The cleaned data is then analyzed and visualized to showcase various aspects of the city's map data, providing insights into the city's layout, points of interest, and other geographical features.
This project conducts a rigorous statistical analysis of the Stroop Effect, a renowned experiment from the field of cognitive psychology, using Python. The project employs statistical methods to analyze and interpret the results of the Stroop Test, a measure of cognitive abilities. The objective is to comprehend the relationship between word reading and color naming performance, offering insights into cognitive processing.
This project involves an intricate analysis of a wine quality dataset using R and advanced data analysis techniques. The project delves into the factors that influence wine quality, employing statistical analysis to unearth hidden insights. Data visualization is harnessed to present the findings in a compelling and informative manner, aiding in the understanding of the complex interplay between different factors and wine quality.