-This folder contains the first project from my CompSci 189 Machine Learning Course. Implemented Support Vector Machines to classify three datasets, 2 of which are multiclass image classification using pixels as the inputs and the third is a binary classifier identifying spam/ham emails. The coding includes data partitioning and preprocessing, building the support vector machines, running cross validation, hyperparameter tuning, and grid search. Achieved 98.5% accuracy on the mnist image classifier model and landing in the top 3% of the class: https://www.kaggle.com/c/hw1-mnist-competition-cs189sp22/leaderboard
-This folder contains assignments from some of my fundamental data science courses. Topics include data cleaning/preprocessing, pandas, SQL, Regex, data visualization tools, linear regression, logistic regression, decision trees, loss functions (optimization of loss functions), bias variance tradeoff, feature engineering, principle component analysis, cross validation, regularization and gradient descent.
-This folder contains assignments in more advanced courses exploring machine learning, data mining, and data analytics in much more depth. Topics include neural networks, sentiment analysis, dimensionality reduction, support vector machines, decision trees, clustering, ensemble methods, boosting, nearest neighbors, cross validation, and error metrics.