Highest R-Score Award in Machine Learning Competition at Rutgers University USACS [Undergraduate Student Alliance of Computer Scientists]

I participated in a Machine Learning Competition @ Rutgers & was the lead programmer for a predictive model for housing prices. 🏠📊

I used: Preprocessing (feature engineering, data scaling & data conversion), Polynomial Features (allowing the model to capture non-linear relationships while keeping the model linear), regularization with Ridge Regression & train/test split for model validation (to reduce overfitting & stabilize coefficient estimation). My Project (all coded in Python) achieved the highest R-squared value (0.75), meaning the model explains 75% of the variance in housing prices - a substantial benchmark! 🎯
Thanks to Gabriele Lisci & Kristen Reilly!

Contact me to view my work or if you have further questions!