lasso-regression

There are 881 repositories under lasso-regression topic.

  • machine-learning

    je-suis-tm/machine-learning

    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 Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression

    Language:Jupyter Notebook2555157

  • gyrdym/ml_algo

    Machine learning algorithms in Dart programming language

    Language:Dart19654633

  • MBKraus/Predicting_real_estate_prices_using_scikit-learn

    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)

    Language:Python16814151

  • bhushan23/ADMM

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

    Language:Jupyter Notebook1573427

  • Somnibyte/MLKit

    A simple machine learning framework written in Swift 🤖

    Language:Swift15212314
  • MLWithPytorch

    Mayurji/MLWithPytorch

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

    Language:Python12911437

  • JuliaAI/MLJLinearModels.jl

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

    Language:Julia8379313

  • englianhu/binary.com-interview-question

    次元期权应征面试题范例。 #易经 #道家 #十二生肖 #姓氏堂号子嗣贞节牌坊 #天文历法 #张灯结彩 #农历 #夜观星象 #廿四节气 #算卜 #紫微斗数 #十二时辰 #生辰八字 #命运 #风水 《始祖赢政之子赢家黄氏江夏堂联富•秦谏——大秦赋》 万般皆下品，唯有读书高。🚩🇨🇳🏹🦔中科红旗，歼灭所有世袭制可兰经法家回教徒巫贼巫婆、洋番、峇峇娘惹。https://gitee.com/englianhu

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  • statistical-python/yaglm

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

    Language:Python58

  • hiroyuki-kasai/SparseGDLibrary

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

    Language:MATLAB552131

  • ncn-foreigners/nonprobsvy

    An R package for modern methods for non-probability samples

    Language:R516355

  • tlverse/hal9001

    🤠 📿 The Highly Adaptive Lasso

    Language:R4944515

  • SravB/Computer-Vision-Weightlifting-Coach

    Analyzes weightlifting videos for correct posture using pose estimation with OpenCV

    Language:Jupyter Notebook45209

  • faizanxmulla/CS2007-machine-learning-techniques

    Repository containing introduction to the main methods and models used in machine learning problems of regression, classification and clustering.

    Language:Jupyter Notebook42107

  • SebastianRokholt/Hybrid-Recommender-System

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

    Language:Jupyter Notebook402315

  • shubhpawar/Automated-Essay-Scoring

    Automated Essay Scoring on The Hewlett Foundation dataset on Kaggle

    Language:Jupyter Notebook323013

  • D2KLab/twitpersonality

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

    Language:Python30959

  • Yunhui-Gao/ISTA

    Iterative shrinkage / thresholding algorithms (ISTAs) for linear inverse problems

    Language:MATLAB30112

  • NikhilaThota/CapstoneProject_House_Prices_Prediction

    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 analysis.

    Language:Jupyter Notebook260013

  • Nemshan/predicting-Paid-amount-for-Claims-Data

    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) skills in a real case analysis setting. You are expected to clean and process data and then apply various ML techniques like Linear and no linear models like regularized regression, MARS, and Partitioning methods. You are expected to use at least two of R, Python and JMP software. Data details: Medical claims file for 2016 contains ~17 millions rows and ~60 columns of data, containing ~6.5 million individual medical claims. These claims are all commercial claims that were filed by healthcare providers in 2016 in the state of NH. These claims were ~88% for residents of NH and the remaining for out of state visitors who sought care in NH. Each claim consists of one or more line items, each indicating a procedure done during the doctor’s visit. Two columns indicating Billed amount and the Paid amount for the care provided, are of primary interest. The main objective is to predict “Paid amount per procedure” by mapping a plethora of features available in the dataset. It is also an expectation that you would create new features using the existing ones or external data sources. Objectives: Step 1: Take a random sample of 1 million unique claims, such that all line items related to each claim are included in the sample. This will result in a little less than 3 million rows of data. Step 2: Clean up the data, understand the distributions, and create new features if necessary. Step 3: Run predictive models using validation method of your choice. Step 4: Write a descriptive report (less than 10 pages) describing the process and your findings.

    Language:Jupyter Notebook242210

  • docnok/detection-estimation-learning

    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.

    Language:Jupyter Notebook23008

  • YunyiShen/CAR-LASSO

    Conditional Auto-Regressive LASSO in R

    Language:C++20355

  • zdq0808/Blasso

    Integrating LASSO and bootstrapping algorithm to find best prognostic or predictive biomarkers

    Language:R20

  • sandipanpaul21/Logistic-regression-in-python

    Logistic Regression technique in machine learning both theory and code in Python. Includes topics from Assumptions, Multi Class Classifications, Regularization (l1 and l2), Weight of Evidence and Information Value

    Language:Jupyter Notebook18007

  • Amitha353/Machine-Learning-Regression

    Machine-Learning-Regression

    Language:Jupyter Notebook171014

  • mntabassm/SAEN-LARS

    Sequential adaptive elastic net (SAEN) approach, complex-valued LARS solver for weighted Lasso/elastic-net problems, and sparsity (or model) order detection with an application to single-snapshot source localization.

    Language:MATLAB16204

  • tweichle/Predicting-Baseball-Statistics

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

    Language:Jupyter Notebook16203

  • MHassaanButt/Antenna-design-using-ML

    In this project, I applied different regression models for rmse and mae on antenna dataset for predict signal strength.

    Language:Jupyter Notebook15103

  • J3FALL/LASSO-Regression

    LASSO Regularization in C++

    Language:C++14115
  • regularized-linear-regression-deep-dive

    wyattowalsh/regularized-linear-regression-deep-dive

    Explanations and Python implementations of Ordinary Least Squares regression, Ridge regression, Lasso regression (solved via Coordinate Descent), and Elastic Net regression (also solved via Coordinate Descent) applied to assess wine quality given numerous numerical features. Additional data analysis and visualization in Python is included.

    Language:Jupyter Notebook13101
  • RelaxedLasso

    continental/RelaxedLasso

    Implementation of Relaxed Lasso Algorithm for Linear Regression.

    Language:Python12304

  • nitya123-github/Concrete-strength

    Predicting the compressive strength of concrete using ML methods and Artificial Nueral Networks. Tools used in this project are Jupyter Notebook, UCI ML repository,Kaggle,Google colab.

    Language:Jupyter Notebook12106

  • ostad-ai/Machine-Learning

    This repository contains topics and codes related to Machine Learning and Data Science, especially in Python

    Language:Jupyter Notebook12100

  • sid321axn/Udacity-MLND-Capstone-Gold-Price-Prediction

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

    Language:Jupyter Notebook121014

  • amayumradia/AirBnB-pricing-prediction

    Harvard Project - Accuracy improvement by adding seasonality premium pricing

    Language:Jupyter Notebook11407

  • DeepthiSudharsan/Rainfall-Pattern-Prediction-using-ML

    Environmental Studies (P/F course) - End Semester Project

    Language:Jupyter Notebook10104