This is my collection of projects from the class "CSE 158: Recommender Systems and Data Mining" at University of California, San Diego.
This course is devoted to applying my knowledge to current methods for recommender systems, data mining, and predictive analytics. The projects that I included here display my application in supervised learning methods such as least-squares regression and the use of training-validation-testing and regularization pipelines, supervised classification models (Logistic Regression, Support Vector Machines, and KNN) and model evalutation using Gradient Descent, dimensionality reduction and clustering (Principal Component Analysis, K-means and hierarchical clustering, and Community Detection), Recommender systems models such as Collaborative Filtering Models and Latent Factor Models, Text Data Mining using BeautifulSoup, and finally Natural Language Processing tools such as Bag of Words, TF-IDF, and Recurrent Neural Networks and LSTMs to perform sentiment analysis.