Library for easy deployment of A-Connect methodology.

Smooth Effects on Response Penalty for CLM

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.

Regularization is a crucial technique in machine learning that helps to prevent overfitting. Overfitting occurs when a model becomes too complex and learns the training data so well that it fails to generalize to new, unseen data.

This project compares the effects of Ridge (L2) and Lasso (L1) regression models on clinical data.

Classification Using Logistic Regression by Making a Neural Network Model. This project also includes comparison of Model performance when different regularization techniques are used

Modeling daily electricity futures return variations from merit-order fundamentals using Huber and Ridge regression

DUA-D2C: Dynamic Uncertainty Aware Method for Overfitting Remediation in Deep Learning

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Your all-in-one Machine Learning resource – from scratch implementations to ensemble learning and real-world model tuning. This repository is a complete collection of 25+ essential ML algorithms written in clean, beginner-friendly Jupyter Notebooks. Each algorithm is explained with intuitive theory, visualizations, and hands-on implementation.