Paula Sasot

I'm building end-to-end data science projects through Kaggle, from data cleaning and exploratory analysis to modeling and communicating results. In House Prices, I developed a Linear Regression baseline with a 0.1305 validation RMSE and am extending it with Random Forest and XGBoost.

I've also used pandas, seaborn, and scipy.stats to answer business questions in a student performance analysis, uncovering relationships between absences, tutoring, music participation, and academic outcomes. Alongside my studies at Universitat de Valencia, I contribute as an outreach volunteer with the IEEE Student Branch UV.