Advanced Applied Statistics
Skills Gained
Statistical Analysis of Housing Prices with Regularized Regression
Statistical Modeling & Regression
Regularization (Ridge, Lasso)
Cross-Validation & Resampling
Feature Engineering & Preprocessing
Applied Data Analysis
Regularization (Ridge, Lasso)
Cross-Validation & Resampling
Feature Engineering & Preprocessing
Applied Data Analysis
2025
This project analyzed residential housing prices using statistical regression methods on the Ames Housing dataset. I applied log-transformation to stabilize variance and built OLS, Ridge, and Lasso regression models to identify key price drivers while addressing multicollinearity and high-dimensional predictors. Models were evaluated using 10-fold cross-validation with RMSE and MSE, alongside bootstrap resampling for uncertainty estimation. Results showed regularized models outperformed OLS, with construction quality, living area, and neighborhood emerging as the strongest predictors of housing prices.
This project analyzed residential housing prices using statistical regression methods on the Ames Housing dataset. I applied log-transformation to stabilize variance and built OLS, Ridge, and Lasso regression models to identify key price drivers while addressing multicollinearity and high-dimensional predictors. Models were evaluated using 10-fold cross-validation with RMSE and MSE, alongside bootstrap resampling for uncertainty estimation. Results showed regularized models outperformed OLS, with construction quality, living area, and neighborhood emerging as the strongest predictors of housing prices.