Kristina Aggas
Data Analytics M.S. Candidate (Slippery Rock University, Class of 2027)
Focus: Predictive Modeling • Statistical Analysis • Python • SQL • SAS
🔍 About This Portfolio
Welcome! This site showcases my analytics, statistics, and machine learning projects created during my Data Analytics M.S. program and independent research.
My work focuses on:
- Predictive modeling & regression
- Data cleaning and feature engineering
- Statistical analysis (ANOVA, hypothesis testing, diagnostics)
- Exploratory data analysis (EDA)
- Comparing methods across Python, SQL, and SAS
- Communicating complex findings clearly
You can also find all source code on my GitHub:
👉 https://github.com/KristinaAggas
⭐ Featured Project
Bike Share Demand – Regression Modeling (2025-11-14)
A full regression analysis exploring how seasonality, weather patterns, and environmental factors influence daily bike rentals.
Highlights:
- Built multiple linear regression models
- Included transformed predictors (e.g., log temperature, humidity)
- Examined multicollinearity, VIF, and diagnostics
- Produced an interpretable final model explaining rental behavior
👉 View project folder
📂 Other Projects
Project – 2025-11-10 - LA Crime Analysis
A Data Camp project where I analyzed Los Angeles crime data to identify patterns in criminal behavior.
👉 Open Folder
Project – 2025-10-12 - Analyzing SAT Scores of New York High School Students
A Data Camp project where I analyzed New York High Schools by their students’ SAT scores.
👉 Open Folder
Project – 2025-10-11 - Sorting 90’s Netflix Movies using Python
A Data Camp project where I conducted an exploratory data analysis to better understand the top 90’s movies on Netflix.
👉 Open Folder
Project – 2025-10-03 - Identifying Gender Differences in Mean Chemical Values using SAS
My first SAS project, where I attempted to determine if significant gender differences exist in the mean values
of calcium, inorganic phosphorus, and alkaline phosphatase in subjects over age 65
👉 Open Folder
🧰 Technical Skills
- Python: pandas, NumPy, Matplotlib, Seaborn, scikit-learn
- SAS: PROC REG, PROC GLM, PROC ANOVA, PROC CORR
- SQL for data extraction, joins, aggregations
- Git & GitHub for version control
- Jupyter Notebook, VS Code, Anaconda
Statistics & Modeling
- Linear regression (simple & multiple)
- Model diagnostics (VIF, residuals, transformations)
- Hypothesis testing
- ANOVA / ANCOVA
- Probability distributions
- Data cleaning & feature engineering
Communication
- Clear explanations of statistical models for non-technical readers
- Report-style summaries and visualizations
- Portfolio-ready project writeups
Feel free to connect if you’d like to talk about analytics, research, or internships.