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A simple machine learning framework written in Swift 🤖

Machine Learning ⭐ 117

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, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic

Mlwithpytorch ⭐ 107

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

Predicting_real_estate_prices_using_scikit Learn ⭐ 59

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)

Machine Learning Regression ⭐ 25

Built house price prediction model using linear regression and k nearest neighbors and used machine learning techniques like ridge, lasso, and gradient descent for optimization in Python

Mdl Complexity ⭐ 15

MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".

Predicting Baseball Statistics ⭐ 14

Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn

Double Descent Curve ⭐ 11

Double Descent Curve with Optical Random Features

Demo from Data Community Bydgoszcz i Toruń, 27.02.2019

Detection Estimation Learning ⭐ 8

Python notebooks for my graduate class on Detection, Estimation, and Learning. Intended for in-class demonstration. Notebooks illustrate a variety of concepts, from hypothesis testing to estimation to image denoising to Kalman filtering. Feel free to use or modify for your instruction or self-study.

Movie Recommendation System ⭐ 8

Movie Recommendation System using the MovieLens dataset

Capstoneproject_house_prices_prediction ⭐ 7

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 suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysi

Nba Draft Model 2018 ⭐ 6

Jupyter notebook that outlines the process of creating a machine learning predictive model. Predicts the peak "Wins Shared" by the current draft prospects based on numerous features such as college stats, projected draft pick, physical profile and age. I try out multiple models and pick the best performing one for the data from my judgement.

Applied Machine Learning ⭐ 6

Applied Machine Learning

Machine Learning Regression Models ⭐ 5

This repository contains only projects using regression analysis techniques. Examples include a comprehensive analysis of retail store expansion strategies using Lasso and Ridge regressions.

Predicting Condominium Price Using Data From Webscraping ⭐ 5

Scrape Bangkok condominium listing from hipflat.com and compare ML performance

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