regularization-hyperparameters

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)

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.

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.

PyInvGeo: Regularized Inversion Techniques

Predicting Forest Fire in Algeria

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.

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

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

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

Salary Prediction Classification:

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

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.

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

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