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

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Applied Machine Learning

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

Use Ridge Regression and Lasso Regression in prostate cancer data

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

Machine Learning Code Implementations in Python

The Cancer Genome Atlas Pan-Cancer analysis project

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

Implementations of Machine Learning models, Regularizers, Optimizers and different Cost functions.

This toolbox offers 7 machine learning methods for regression problems.

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

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

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.

This project develops a machine learning model to predict the salaries of baseball players based on their past performance.