ridge-regression
There are 714 repositories under ridge-regression topic.
facebookexperimental/Robyn
Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
JuliaStats/MultivariateStats.jl
A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)
YiranJing/Coronavirus-Epidemic-COVID-19
👩🏻⚕️Covid-19 estimation and forecast using statistical model; 新型冠状病毒肺炎统计模型预测 (Jan 2020)
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
Somnibyte/MLKit
A simple machine learning framework written in Swift 🤖
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)
bhushan23/ADMM
Implemented ADMM for solving convex optimization problems such as Lasso, Ridge regression
Mayurji/MLWithPytorch
Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch
agrawal-priyank/machine-learning-regression
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
JuliaAI/MLJLinearModels.jl
Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)
mickcrosse/mTRF-Toolbox
A MATLAB package for modelling multivariate stimulus-response data
m-clark/models-by-example
By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.
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
SravB/Computer-Vision-Weightlifting-Coach
Analyzes weightlifting videos for correct posture using pose estimation with OpenCV
getzze/RobustModels.jl
A Julia package for robust regressions using M-estimators and quantile regressions
shubhpawar/Automated-Essay-Scoring
Automated Essay Scoring on The Hewlett Foundation dataset on Kaggle
buus2/dcbit
Demo from Data Community Bydgoszcz i Toruń, 27.02.2019
lightonai/double-descent-curve
Double Descent Curve with Optical Random Features
faizanxmulla/CS2007-machine-learning-techniques
Repository containing introduction to the main methods and models used in machine learning problems of regression, classification and clustering.
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.
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.
alexvlis/movie-recommendation-system
Movie Recommendation System using the MovieLens dataset
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.
schatzlab/scikit-ribo
Accurate estimation and robust modelling of translation dynamics at codon resolution
SteffenMoritz/ridge
CRAN R Package: Ridge Regression with automatic selection of the penalty parameter
csinva/mdl-complexity
MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".
faosorios/fastmatrix
Fast computation of some matrices useful in statistics
Kennethborup/self_distillation
Self-Distillation with weighted ground-truth targets; ResNet and Kernel Ridge Regression
mlesnoff/rchemo
Archived repo (see Readme) - R package for regression and discrimination, with special focus on chemometrics and high-dimensional data.
tweichle/Predicting-Baseball-Statistics
Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn and TensorFlow-Keras
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
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.
ekapope/Predicting-condominium-price-using-data-from-webscraping
Scrape Bangkok condominium listing from hipflat.com and compare ML performance
hanfang/glmnet_py
glmnet for python
mdpetters/RegularizationTools.jl
A Julia package to perform Tikhonov regularization for small to moderate size problems.
spiningup/Machine-learning-methods-for-materials-science
Evaluate various supervised learning methods to predict cohesive energies of solids (kernel ridge regression is the best)