- Extracted macroeconomic indicators from FRED
- Selected features and trained Logistic Regression model
- Achieved Area Under Curve: 0.94
- Created industry indexes based on S&P500 prises and market capitalization
- Applied CPPI model using STIP ETF as a "safe" asset
- Compared price returns of the combined portfolio to the "risky" (equity only) portfolio
- Achieved significant max drawdown reduction (38% for "Energy Minerals")
- Created Logistic Regression models predicting Positive/Negative labels for Amazon reviews of acrylic markers
- Engineered features using N-grams and Tfidf
- Optimized using GridsearchCV to reach the best model
- Achieved Area Under Curve: 0.90