Trained/optimized ML models (Logistic, Random Forest, and LGBM Classifiers) to sort Spotify tracks into genres. Correctly classified 90% of tracks, a 67% improvement from the baseline, and achieved 90% precision, recall, and f1 scores.
Python, Scikit-learn
Solved various ML problems including: sentiment analysis, spam classification, churn/survival, and time series forecasting. Explored data pipelines, hyperparameter optimization, ensembles, transfer learning, and unsupervised learning.
Python, Scikit-learn
Employs multivariate regularized regression to predict car resale value, achieving an R² of 90.27% on test data.
Python, Scikit-learn
Notes and project work from Stanford's ML Course taught by Professor Andrew Ng on Coursera.
Python, NumPy
Predicts whether a borrower will default on their loan using K Nearest Neighbors (77.813% accuracy).
Python, Scikit-learn