NCCU Master's Thesis: Text Mining on FOMC Minutes
▶ Supervised by Statistics' Prof. Yu, Ching-Hsiang
• Applied dimension reduction techniques and machine learning algorithms (LR, SVM, RF, & XGBoost) to classify FOMC texts into 3 categories, resulting in a test accuracy of 88.11%
• Utilized PyTorch and word2vec to analyze 9 economic keywords related to the Fed's 3 primary mandates, revealing the Fed's emphasis on ethnic unemployment rates during interest rate adjustments
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[Master's Thesis] NCCU Master's Thesis: Text Mining on FOMC Minutes (Supervised by Statistics' Prof. Yu, Ching-Hsiang)
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