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

Accurate estimation and robust modelling of translation dynamics at codon resolution

Movie Recommendation System using the MovieLens dataset

Evaluate various supervised learning methods to predict cohesive energies of solids (kernel ridge regression is the best)

glmnet for python

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

Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data

A Genetic Algorithm (GA) / Discrete Particle Swarm Optimization/ Hybrid (GA-PSO) for nuclear fuel optimization using ML surrogates (DNN, KNN, Random Forest, Ridge) and OpenMC. Optimizes fuel loading patterns for a target k-eff and minimal Power Peaking Factor (PPF).

Implementation of some Machine Learning Algorithms from scratch

Python implementations of semiparametric statistical techniques.

The Cancer Genome Atlas Pan-Cancer analysis project

OLS (twoway clustered standard errors), Imperfect Multicollinearity (Ridge and PCA), ARMA(p,q) with Bootstrap

Machine Learning Project to predict FWI value using Ridge Regression

Here I upload my ML test scripts written in MATLAB or Python

This toolbox offers 7 machine learning methods for regression problems.