Projects for Udacity's machine learning nanodegree foundations
Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.
Investigated factors that affect the likelihood of charity donations being made based on real census data. Developed a naive classifier to compare testing results to. Trained and tested several supervised machine learning models on preprocessed census data to predict the likelihood of donations. Selected the best model based on accuracy, a modified F-scoring metric, and algorithm efficiency.
Reviewed unstructured data to understand the patterns and natural categories that the data fits into. Used multiple algorithms and both empirically and theoretically compared and contrasted their results. Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervised analysis.
Built a recommendation engine based on "Collaborative Filtering". Explored how similar and dissimilar people's taste in movies are based on how they rate different movies.
Applied PCA on the "Labeled faces in the wild" dataset to obtain the most significant eigenfaces/dimensionality reduction