Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

A python package for penalized generalized linear models that supports fitting and model selection for structured, adaptive and non-convex penalties.

TwitPersonality: Computing Personality Traits from Tweets using Word Embeddings and Supervised Learning

Implementation of Relaxed Lasso Algorithm for Linear Regression.

Analysis of NBA player stats and salaries of the 2016-17 for the 17-18 season

For quick search

Drop-in replacement of sklearn's Linear Regression with coefficients constraints

Applied Machine Learning

Use Ridge Regression and Lasso Regression in prostate cancer data

Machine Learning Code Implementations in Python

Evaluating multiple classifiers after SVM-RFE (Support Vector Machine-Recursive Feature Elimination)

The Cancer Genome Atlas Pan-Cancer analysis project

TNO PET Lab - secure Multi-Party Computation (MPC) - MPyC - Secure Learning

Nested Cross-Validation for Bayesian Optimized Linear Regularization

Predicting hospital costs using Linear Regression and using feature engineering to improve the model performance.

This toolbox offers 7 machine learning methods for regression problems.

Forecasting the likelihood of a customer defaulting their auto loan using classification models

Regression algorithm implementaion from scratch with python (OLS, LASSO, Ridge, robust regression)

Boston Housing Analysis: This repo presents an in-depth analysis of the Boston Housing dataset using Linear, Lasso, and Ridge Regression models. It explores data, preprocesses features, visualizes relationships, and evaluates model performance.