A Multiple Linear Regression model built using the PACE framework to predict NYC taxi fare amounts. Achieving an R² of 0.748, this project focuses on end-to-end data analytics, from outlier handling in EDA to model evaluation and business impact analysis.

Data Analyst portfolio · Python · ML · BI · Google Certified · PACE framework

Customer segmentation (K-Means) + profit modeling (Ridge+CV) · PACE framework · Statistical validation

Kaggle competition entry · XGBoost · 88.7% AUC-ROC · PACE framework

Capstone project from Google Advanced Data Analytics Certificate. - 14,999 employees analyzed - Random Forest + SMOTE, 98% accuracy, ROC-AUC 0.997 - Top driver: satisfaction_level, tenure (3-year cliff), workload