• Selected from a pool of 1600+ applicants for a year-long fellowship to study machine learning and address business challenges. Tasked with contributing to the Allstate Wildfire Incidence and Property Insurance: A Data-Driven Analysis project.
• Utilized Python libraries such as Pandas, NumPy, MatplotLib, and Seaborn to clean and prepare data, enabling feature engineering and deep learning model creation within a community of over 200 members.
• Collaborated with a team of 5 fellows, employing the CRISP-DM methodology to tackle complex business issues and deliver effective machine learning solutions.
• Conducted extensive evaluations and experiments using feature engineering and deep learning techniques like Neural Networks and Gradient Boosting to optimize predictive models.
