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)

Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, F…

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

MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3

次元期权应征面试题范例。

Implemented ADMM for solving convex optimization problems such as Lasso, Ridge regression

Machine learning algorithms in Dart programming language

For quick search

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

🤠 📿 The Highly Adaptive Lasso

Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)

Capstone Project Gold Price Prediction using Machine learning Approach for Udacity Machine Learning engineer Nanodegree Program

A simple machine learning framework written in Swift 🤖

A repository for a machine learning project about developing a hybrid movie recommender system.

Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and s…

Automated Essay Scoring on The Hewlett Foundation dataset on Kaggle

Machine-Learning-Regression

Ridge and Lasso Regression - A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price. For the same purpose, the company has collected a data set from the sale of houses in Australia.

Introduction The context is the 2016 public use NH medical claims files obtained from NH CHIS (Comprehensive Health Care Information System). The dataset contains Commercial Insurance claims, and a small fraction of Medicaid and Medicare payments for dually eligible people. The primary purpose of this assignment is to test machine learning (ML) …

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