7 results for “topic:ridge-and-lasso-regression”
This collection features a wide array of simple implementations of machine learning algorithms spanning various methodologies and applications.
WineQualityPredictorML is a comprehensive machine learning toolkit for predicting and assessing wine quality. Explore advanced regression models, SVM, and decision trees for insightful wine quality analysis.
Ridge Regularization (L2) and Ridge Regression, built entirely from scratch using both closed‑form solutions and gradient descent.
Built a machine learning model to find the factors influencing house prices in the USA. Incorporated cross validation with Ridge and Lasso regularization to determine the factors impacting the price. Achieved AUC scores of 91.1% and 91.3% for ridge and lasso regularizations, respectively.
streamlit
Machine Learning Projects
Predict the quality of red wine based on physicochemical features using various regression models including Linear Regression, Ridge, Lasso, Random Forest, and XGBoost. The project also includes hyperparameter tuning and model export capabilities.