Machine Learning for Policy Research
As part of my coursework in machine learning at the University of Chicago, I explored diverse predictive models such as Linear Regression, Random Forest, and XGBoost to solve real-world policy problems. Projects included forecasting voter turnout, modeling COVID-19 impacts, and identifying tax evasion trends. This hands-on experience honed my skills in applying advanced algorithms, cross-validation, and hyperparameter tuning to generate actionable insights in public policy.
Topic Covered:
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Statistical Learning
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Linear Regression & Beyond Linearity
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Classification
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Resampling Methods
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Linear Model Selection & Regularization
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Tree-Based Methods
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Support Vector Machines
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Deep Learning
Tool Used:
- Python (Scikit-learn, XGBoost, Pandas, NumPy, Matplotlib, Seaborn, Statsmodels, Joblib, Keras, imbalanced-learn)
Explore the projects on GitHub