Pytorch implementation of λOpt: Learn to Regularize Recommender Models in Finer Levels, KDD 2019

PyInvGeo: Regularized Inversion Techniques

This is Collection of Regularization Deep learning techniques with code and paper

This project explores ML techniques across classification and regression. It includes penguin species classification, breast cancer prediction, and baseball performance prediction using regularization. After, I will develop an XGBoost model for hotel cancellation prediction, analyzing key booking factors and optimizing performance. (In Progress)

This Jupyter Notebook demonstrates hyperparameter tuning for a Logistic Regression model using Python, with a focus on regularization techniques (L1 and L2). It explains how tuning parameters impacts model performance and helps prevent overfitting in classification tasks.

Predicting Forest Fire in Algeria

Salary Prediction Classification:

This is an expansion of dsb318-group4 (see repo: dsb318-group4), in which we collaborated to predict high school graduation rates in CA from other trends (e.g., poverty rate, availability of e-cigarettes). Collaboration between Eli and Emily.

This is a classification model implementation using Random Forest and Logistic Regression in Python and Spark. Originally implemented via AWS EMR Clusters.

Deep Learning project about the design and training of a model for Image Classification

Machine Learning End-to-End (Linear Regression) model project on MS Application Prediction

A computational benchmarking framework for epidemiological inference. Evaluates spline-based Generalized Profiling and Chi-Square fitting against standard NLS to overcome structural misspecification and improve forecasting in time-varying SIR models.

An online course on ML taught by Andrew Ng. Introduces algorithms from scratch including regression models, classification, Neural Networks, SVMs, K-Means clustering, and applications such as Photo OCR.

An effective way to avoid (or at least to reduce) overfitt