lasso-regression

There are 3 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
naive-bayes support-vector-machine stochastic-gradient-descent batch-gradient-descent newton-method multinomial-naive-bayes linear-discriminant-analysis sequential-minimal-optimization k-nearest-neighbors dbscan expectation-maximization expectation-maximization-algorithm factor-analysis independent-component-analysis latent-semantic-analysis low-rank-approximation singular-value-decomposition ridge-regression lasso-regression em-algorithm
Language:Jupyter Notebook 255
gyrdym / ml_algo
Machine learning algorithms in Dart programming language
dartlang dart machine-learning linear-regression sgd stochastic-gradient-descent batch-gradient-descent mini-batch-gradient-descent machine-learning-algorithms algorithm lasso-regression logistic-regression softmax softmax-regression softmax-classifier softmax-algorithm regression hyperparameters data-science classifier
Language:Dart 193
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)
predicting-housing-prices real-estate machine-learning python knn knn-regression lasso-regression lasso ridge-regression decision-trees random-forest neural-network mlp-regressor ols polynomial-regression amsterdam multi-layer-perceptron xgboost polynomial ensemble-learning
Language:Python 168
Somnibyte / MLKit
A simple machine learning framework written in Swift 🤖
regression ridge-regression swift machine-learning machine-learning-algorithms artificial-intelligence polynomial-regression linear-regression machine-learning-library mlkit kmeans-clustering neural-network feedforward-neural-network backpropagation lasso-regression kmeans genetic-algorithm
Language:Swift 152
bhushan23 / ADMM
Implemented ADMM for solving convex optimization problems such as Lasso, Ridge regression
convex-optimization admm lasso-regression ridge-regression pytorch
Language:Jupyter Notebook 151
MLWithPytorch
Mayurji / MLWithPytorch
Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch
pytorch-implementation pytorch machine-learning-algorithms python3 kmeans-clustering linear-regression logistic-regression decision-trees naive-bayes-classifier svm-classifier pca-analysis tfidf-vectorizer naive-bayes-algorithm knn-classification gaussian-mixture-models lasso-regression ridge-regression machine-learning adaboost-algorithm
Language:Python 129
JuliaAI / MLJLinearModels.jl
Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)
elastic-net julia-language lasso-regression logistic-regression mlj multinomial-regression regression-models regressions ridge-regression robust-regression sparse-regression
Language:Julia 81
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
bayesian investment betting statistics lasso-regression ridge-regression elastic-net mcmc garch ets arima kelly-criterion investment-portfolio investment-strategies markowitz-portfolio acd dao daojiao qinren qinrenmuma
Language:HTML 66
yaglm / yaglm
A python package for penalized generalized linear models that supports fitting and model selection for structured, adaptive and non-convex penalties.
statistics machine-learning penalized-regression generalized-linear-models lasso-regression optimization-algorithms tuning-algorithm
Language:Python 56
hiroyuki-kasai / SparseGDLibrary
MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3
optimization optimization-algorithms machine-learning-algorithms machine-learning big-data gradient-descent sparse-linear-solver sparse-regression lasso-regression lasso elasticnet solver algorithms admm proximal-algorithms proximal-operators logistic-regression matrix-completion coordinate-descent support-vector-machines
Language:MATLAB 53
tlverse / hal9001
🤠 📿 The Highly Adaptive Lasso
machine-learning-algorithms nonparametric-regression cross-validation lasso-regression
Language:R 49
ncn-foreigners / nonprobsvy
An R package for modern methods for non-probability samples
lasso-regression nonprobability-sampling survey inverse-probability-weights propensity-scores r
Language:R 47
SravB / Computer-Vision-Weightlifting-Coach
Analyzes weightlifting videos for correct posture using pose estimation with OpenCV
computer-vision machine-learning lasso-regression ridge-regression random-forest elastic-net logistic-regression weightlifting keypoint-detection pose-estimation mpii-dataset ensemble-learning opencv openpose
Language:Jupyter Notebook 43
SebastianRokholt / Hybrid-Recommender-System
A repository for a machine learning project about developing a hybrid movie recommender system.
recommender-system hybrid-recommender-system collaborative-filtering content-based-filtering python matplotlib data-science machine-learning regression-models k-nearest-neighbours movielens lasso-regression randomforest movies movies-recommendation
Language:Jupyter Notebook 40
faizanxmulla / CS2007-machine-learning-techniques
Repository containing introduction to the main methods and models used in machine learning problems of regression, classification and clustering.
classification clustering regression decision-trees iitmadrasonlinedegree knn lasso-regression least-square-regression naive-bayes-classifier neural-network perceptron random-forest ridge-regression
Language:Jupyter Notebook 37
shubhpawar / Automated-Essay-Scoring
Automated Essay Scoring on The Hewlett Foundation dataset on Kaggle
natural-language-processing machine-learning automatic-essay-scoring linear-regression ridge-regression lasso-regression support-vector-regression gradient-boosting-regressor nltk sklearn ensemble-learning cohens-kappa text-regression text-processing text-analytics
Language:Jupyter Notebook 32
D2KLab / twitpersonality
TwitPersonality: Computing Personality Traits from Tweets using Word Embeddings and Supervised Learning
big5 lasso-regression natural-language-processing personality-traits python regression svm tweepy twitter wordembeddings
Language:Python 30
Yunhui-Gao / ISTA
Iterative shrinkage / thresholding algorithms (ISTAs) for linear inverse problems
proximal-algorithms lasso-regression image-denoising proximal-gradient-method matlab
Language:MATLAB 29
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.
python ridge-regression linear-regression data-visualization data-analysis datawrangling predictive-modeling machine-learning correlation standardization data-extraction data-exploration encoding-library regression-analysis lasso-regression regularization root-mean-squared-error-metric parameter-tuning cross-validation k-fold
Language:Jupyter Notebook 25
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.
python predictive-modeling ridge-regression lasso-regression random-forest earth
Language:Jupyter Notebook 23
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.
detection estimation-theory machine-learning python python-notebook jupyter-notebook teaching-materials estimation regression kalman-filter hypothesis-testing signal-processing ridge-regression lasso lasso-regression tikhonov-regularization maximum-likelihood
Language:Jupyter Notebook 22
DongqiangZeng0808 / Blasso
Integrating LASSO and bootstrapping algorithm to find best prognostic or predictive biomarkers
lasso-regression
Language:R 20
YunyiShen / CAR-LASSO
Conditional Auto-Regressive LASSO in R
gaussian-field lasso-regression bayesian markov-random-field
Language:C++ 20
Amitha353 / Machine-Learning-Regression
Machine-Learning-Regression
simple-linear-regression multiple-regression gradient-descent coordinate-descent ridge-regre lasso-regression nearest-neighbor-search kernel-regression loss-functions bias-variance-tradeoff cross-validation generalization-error overfitting coefficient-path polynomial-regression
Language:Jupyter Notebook 17
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
logistic-regression regularization l1-regularization l2-regularization lasso-regression ridge-regression elastic-net-regression multiclass-logistic-regression weight-of-evidence information-value logistic-regression-assumptions
Language:Jupyter Notebook 17
MHassaanButt / Antenna-design-using-ML
In this project, I applied different regression models for rmse and mae on antenna dataset for predict signal strength.
linearregression elasticnet decisiontreeregressor randomforestregressor lasso-regression
Language:Jupyter Notebook 16
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.
adaptive-learning complex-valued-data elastic-net lasso-regression regularized-linear-regression regularization-paths solution-path sparse-reconstruction sparse-regression sparse-regularization matlab-toolbox acoustic-model compressed-beamforming direction-of-arrival source-localization
Language:MATLAB 16
tweichle / Predicting-Baseball-Statistics
Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn and TensorFlow-Keras
machine-learning data-visualization supervised-learning predictive-modeling exploratory-data-analysis logistic-regression regularization cross-validation python data-science oversampling decision-trees random-forests support-vector-machines xgboost linear-regression ridge-regression lasso-regression elastic-net k-nearest-neighbors
Language:Jupyter Notebook 16
J3FALL / LASSO-Regression
LASSO Regularization in C++
lasso lasso-regression regularization polynomial-regression
Language:C++ 14
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.
regularized-linear-regression lasso-regression ridge-regression elastic-net-regression coordinate-descent optimization python jupyter-notebook ordinary-least-squares regularization regression
Language:Jupyter Notebook 13
RelaxedLasso
continental / RelaxedLasso
Implementation of Relaxed Lasso Algorithm for Linear Regression.
coordinate-descent lars lasso lasso-regression linear-regression regularization relaxed relaxed-lasso
Language:Python 12
amayumradia / AirBnB-pricing-prediction
Harvard Project - Accuracy improvement by adding seasonality premium pricing
scikit-learn airbnb-pricing-prediction airbnb-dataset linear-regression premium-pricing ridge-regression lasso-regression regularization k-fold python
Language:Jupyter Notebook 11
DeepthiSudharsan / Rainfall-Pattern-Prediction-using-ML
Environmental Studies (P/F course) - End Semester Project
rainfall-prediction machine-learning lasso-regression random-forest-regression linear-regression
Language:Jupyter Notebook 10
joeyism / nnnba
Analysis of NBA player stats and salaries of the 2016-17 for the 17-18 season
player ridge lasso find-undervalued-players nba nba-stats nba-analytics nba-visualization nba-stats-api nba-players nba-prediction nba-data machine learning lasso-regression linear regression elasticnet
Language:Python 10
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.
deep-nueral-networks linear-regression random-forest lasso-regression mlp-regressor
Language:Jupyter Notebook 10
sid321axn / Udacity-MLND-Capstone-Gold-Price-Prediction
Capstone Project Gold Price Prediction using Machine learning Approach for Udacity Machine Learning engineer Nanodegree Program
machine-learning stock-price-prediction gold udacity-machine-learning-nanodegree udacity-nanodegree python numpy pandas matplotlib-pyplot sklearn-estimator ensemble-learning ensemble-model support-vector-regression random-forest lasso-regression ridge-regression gridsearchcv
Language:Jupyter Notebook 10

lasso-regression

je-suis-tm / machine-learning

gyrdym / ml_algo

MBKraus / Predicting_real_estate_prices_using_scikit-learn

Somnibyte / MLKit

bhushan23 / ADMM

Mayurji / MLWithPytorch

JuliaAI / MLJLinearModels.jl

englianhu / binary.com-interview-question

yaglm / yaglm

hiroyuki-kasai / SparseGDLibrary

tlverse / hal9001

ncn-foreigners / nonprobsvy

SravB / Computer-Vision-Weightlifting-Coach

SebastianRokholt / Hybrid-Recommender-System

faizanxmulla / CS2007-machine-learning-techniques

shubhpawar / Automated-Essay-Scoring

D2KLab / twitpersonality

Yunhui-Gao / ISTA

NikhilaThota / CapstoneProject_House_Prices_Prediction

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

docnok / detection-estimation-learning

DongqiangZeng0808 / Blasso

YunyiShen / CAR-LASSO

Amitha353 / Machine-Learning-Regression

sandipanpaul21 / Logistic-regression-in-python

MHassaanButt / Antenna-design-using-ML

mntabassm / SAEN-LARS

tweichle / Predicting-Baseball-Statistics

J3FALL / LASSO-Regression

wyattowalsh / regularized-linear-regression-deep-dive

continental / RelaxedLasso

amayumradia / AirBnB-pricing-prediction

DeepthiSudharsan / Rainfall-Pattern-Prediction-using-ML

joeyism / nnnba

nitya123-github / Concrete-strength

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