This repo includes the beginning of my more advanced project work during my undergraduate program at UPenn, wherein we applied statistical concepts to analysis in R. Below is a summary of those projects and the skills that each project demonstrates:
1. Fantasy Football Projection Analysis
- Skills: Data wrangling, statistical modeling, regression analysis, predictive analytics, model validation.
- Takeaways: Developed predictive models to forecast fantasy football player performance, refined models through cross-validation, and improved forecasting accuracy using historical player data and game statistics.
2. Real Estate Price Prediction
- Skills: Machine learning, feature engineering, data visualization, regression analysis, model selection.
- Takeaways: Created and evaluated machine learning models to predict real estate prices, applied feature selection techniques to enhance model accuracy, and visualized the relationship between housing features and market prices.
3. Presidential Candidates and Public Perception of Government Corruption
- Skills: Survey data analysis, hypothesis testing, linear regression, data visualization, binary classification.
- Takeaways: Analyzed the relationship between public sentiment toward presidential candidates and beliefs about government corruption, revealing significant correlations and providing insights into how political leanings impact perceptions of government integrity.
4. Analyzing Player Performance in the NBA
- Skills: Exploratory data analysis (EDA), correlation analysis, predictive modeling, time series analysis.
- Takeaways: Investigated key performance indicators (KPIs) for NBA players, identifying statistical patterns that contribute to player success, and developed models to predict future performance based on historical data.