I am Buddhika Patalee, a data enthusiast and quantitative researcher with expertise in machine learning, predictive analytics, and econometric modeling. With a Ph.D. in Applied Economics and a strong foundation in data science, I specialize in leveraging advanced statistical techniques and machine learning models to extract insights from complex datasets.
Currently, I am a post-doctoral scholar at the University of Kentucky, where I develop predictive models, machine learning algorithms, and econometric frameworks to analyze large-scale datasets. My work includes data-driven decision-making, policy evaluation, and business intelligence solutions.
My goal is to bridge applied research and real-world applications, helping stakeholders make data-driven decisions using advanced analytics techniques.
AI & Machine Learning Applications
My research integrates applied AI and machine learning techniques to generate consumer insights, model behavior, and support data-driven decision-making. I focus on interpretable, actionable models rooted in economic and behavioral science contexts.
Key Areas of Experience:
Neural Networks & Predictive Modeling: Applied shallow neural networks for time series forecasting and behavioral prediction using tools such as
scikit-learnand R.Consumer Insight Modeling: Used machine learning to model consumer preferences and purchase behavior from panel data.
Survey-Based ML Pipelines: Integrated ML models with survey data to assess decision-making patterns and risk perceptions.
Feature Engineering & Data Cleaning: Built high-quality datasets for supervised learning tasks in Python and R.
Model Evaluation: Applied classification metrics (accuracy, recall, F1), cross-validation, and interpretability tools (feature importance, partial dependence).
Tools & Libraries:
Python (scikit-learn, pandas, matplotlib, seaborn), R, SQL, STATA
