lasso-regression | Topic | Ecosyste.ms: Repos

Topic: "lasso-regression"

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 Notebook - Size: 7.84 MB - Last synced at: about 1 month ago - Pushed at: over 2 years ago - Stars: 234 - Forks: 51

gyrdym/ml_algo

Machine learning algorithms in Dart programming language

Language: Dart - Size: 9.49 MB - Last synced at: 13 days ago - Pushed at: 13 days ago - Stars: 192 - Forks: 31

Somnibyte/MLKit

A simple machine learning framework written in Swift 🤖

Language: Swift - Size: 3.28 MB - Last synced at: 6 days ago - Pushed at: over 6 years ago - Stars: 152 - Forks: 14

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: Python - Size: 870 KB - Last synced at: over 1 year ago - Pushed at: about 6 years ago - Stars: 142 - Forks: 51

Mayurji/MLWithPytorch

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

Language: Python - Size: 868 KB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 122 - Forks: 37

bhushan23/ADMM

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

Language: Jupyter Notebook - Size: 735 KB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 107 - Forks: 27

JuliaAI/MLJLinearModels.jl

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

Language: Julia - Size: 583 KB - Last synced at: 7 days ago - Pushed at: about 1 year ago - Stars: 81 - Forks: 13

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

Language: HTML - Size: 11.4 GB - Last synced at: 21 days ago - Pushed at: 21 days ago - Stars: 66 - Forks: 29

tlverse/hal9001

🤠 📿 The Highly Adaptive Lasso

Language: R - Size: 13.1 MB - Last synced at: 8 days ago - Pushed at: 6 months ago - Stars: 50 - Forks: 15

ncn-foreigners/nonprobsvy

An R package for modern methods for non-probability samples

Language: R - Size: 50.3 MB - Last synced at: 6 days ago - Pushed at: 6 days ago - Stars: 48 - Forks: 5

SravB/Computer-Vision-Weightlifting-Coach

Analyzes weightlifting videos for correct posture using pose estimation with OpenCV

Language: Jupyter Notebook - Size: 24 MB - Last synced at: 4 days ago - Pushed at: almost 6 years ago - Stars: 43 - Forks: 8

yaglm/yaglm

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

Language: Python - Size: 1.73 MB - Last synced at: almost 2 years ago - Pushed at: over 2 years ago - Stars: 41 - Forks: 12

hiroyuki-kasai/SparseGDLibrary

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

Language: Matlab - Size: 22.3 MB - Last synced at: about 2 years ago - Pushed at: over 6 years ago - Stars: 41 - Forks: 27

SebastianRokholt/Hybrid-Recommender-System

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

Language: Jupyter Notebook - Size: 13.5 MB - Last synced at: about 1 year ago - Pushed at: over 3 years ago - Stars: 35 - Forks: 13

shubhpawar/Automated-Essay-Scoring

Automated Essay Scoring on The Hewlett Foundation dataset on Kaggle

Language: Jupyter Notebook - Size: 8.57 MB - Last synced at: almost 2 years ago - Pushed at: about 7 years ago - Stars: 30 - Forks: 13

D2KLab/twitpersonality

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

Language: Python - Size: 2.35 MB - Last synced at: over 1 year ago - Pushed at: over 6 years ago - Stars: 25 - Forks: 9

faizanxmulla/machine-learning-techniques

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

Language: Jupyter Notebook - Size: 8.63 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 20 - Forks: 7

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 Notebook - Size: 7.91 MB - Last synced at: over 1 year ago - Pushed at: over 7 years ago - Stars: 18 - Forks: 13

YunyiShen/CAR-LASSO

Conditional Auto-Regressive LASSO in R

Language: C++ - Size: 284 MB - Last synced at: 8 months ago - Pushed at: over 2 years ago - Stars: 17 - Forks: 5

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 Notebook - Size: 1.4 MB - Last synced at: over 1 year ago - Pushed at: over 6 years ago - Stars: 17 - Forks: 11

Yunhui-Gao/ISTA

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

Language: MATLAB - Size: 3.15 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 15 - Forks: 1

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 Notebook - Size: 3.29 MB - Last synced at: about 2 years ago - Pushed at: about 7 years ago - Stars: 15 - Forks: 8

tweichle/Predicting-Baseball-Statistics

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

Language: Jupyter Notebook - Size: 19.1 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 14 - Forks: 4

DongqiangZeng0808/Blasso

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

Language: R - Size: 3.4 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 13 - Forks: 5

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 Notebook - Size: 51 MB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 13 - Forks: 1

Amitha353/Machine-Learning-Regression

Machine-Learning-Regression

Language: Jupyter Notebook - Size: 10 MB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 13 - Forks: 12

J3FALL/LASSO-Regression

LASSO Regularization in C++

Language: C++ - Size: 2.25 MB - Last synced at: over 1 year ago - Pushed at: over 6 years ago - Stars: 13 - Forks: 5

joeyism/nnnba

Analysis of NBA player stats and salaries of the 2016-17 for the 17-18 season

Language: Python - Size: 51.4 MB - Last synced at: 21 days ago - Pushed at: almost 8 years ago - Stars: 10 - Forks: 3

amayumradia/AirBnB-pricing-prediction

Harvard Project - Accuracy improvement by adding seasonality premium pricing

Language: Jupyter Notebook - Size: 46.6 MB - Last synced at: over 1 year ago - Pushed at: over 8 years ago - Stars: 10 - Forks: 5

continental/RelaxedLasso 📦

Implementation of Relaxed Lasso Algorithm for Linear Regression.

Language: Python - Size: 796 KB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 9 - Forks: 3

SSQ/Coursera-UW-Machine-Learning-Regression

For quick search

Language: Python - Size: 56 MB - Last synced at: about 2 years ago - Pushed at: over 6 years ago - Stars: 9 - Forks: 14

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 Notebook - Size: 5.21 MB - Last synced at: about 2 years ago - Pushed at: over 6 years ago - Stars: 8 - Forks: 14

tsitsimis/constrainedlr

Drop-in replacement of sklearn's Linear Regression with coefficients constraints

Language: Python - Size: 637 KB - Last synced at: 14 days ago - Pushed at: about 1 year ago - Stars: 7 - Forks: 0

3zhang/Python-Lasso-ElasticNet-Ridge-Regression-with-Customized-Penalties

An extension of sklearn's Lasso/ElasticNet/Ridge model to allow users to customize the penalties of different covariates. Works similar to penalty.factor parameter in R's glmnet.

Language: Jupyter Notebook - Size: 1.48 MB - Last synced at: 6 months ago - Pushed at: about 2 years ago - Stars: 7 - Forks: 0

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 Notebook - Size: 786 KB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 7 - Forks: 2

benkeser/halplus

Nonparametric regression and prediction using the highly adaptive lasso algorithm

Language: R - Size: 103 KB - Last synced at: about 1 month ago - Pushed at: over 7 years ago - Stars: 7 - Forks: 7

hkiang01/Applied-Machine-Learning

Applied Machine Learning

Language: Python - Size: 58.8 MB - Last synced at: about 2 years ago - Pushed at: almost 9 years ago - Stars: 7 - Forks: 4

JayantGoel001/Machine-Learning-And-Big-Data

Language: Jupyter Notebook - Size: 12.1 MB - Last synced at: 9 days ago - Pushed at: over 3 years ago - Stars: 6 - Forks: 0

DeepthiSudharsan/Rainfall-Pattern-Prediction-using-ML

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

Language: Jupyter Notebook - Size: 613 KB - Last synced at: almost 2 years ago - Pushed at: almost 4 years ago - Stars: 6 - Forks: 1

bhattbhavesh91/lasso-regression-python

This repository shows how Lasso Regression selects correlated predictors

Language: Jupyter Notebook - Size: 50.8 KB - Last synced at: 23 days ago - Pushed at: almost 4 years ago - Stars: 6 - Forks: 7

marcotav/machine-learning-regression-models

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

Language: Jupyter Notebook - Size: 1000 KB - Last synced at: over 1 year ago - Pushed at: almost 7 years ago - Stars: 6 - Forks: 3

DataSpieler12345/python-for-ds-ml

My Python learning experience 📚🖥📳📴💻🖱✏

Language: Jupyter Notebook - Size: 200 MB - Last synced at: about 1 month ago - Pushed at: 12 months ago - Stars: 5 - Forks: 0

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 Notebook - Size: 29.4 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 5 - Forks: 1

celiaescribe/BDcocolasso

Implementation of the block-descent-CoCoLasso, inspired from the article https://arxiv.org/pdf/1510.07123.pdf

Language: R - Size: 85.2 MB - Last synced at: over 1 year ago - Pushed at: over 5 years ago - Stars: 5 - Forks: 8

jolars/sgdnet

Fast Sparse Linear Models for Big Data with SAGA

Language: R - Size: 2.54 MB - Last synced at: 19 days ago - Pushed at: over 5 years ago - Stars: 5 - Forks: 2

fby1997/Lasso-Regression-coordinate-gradient-descent-proximal-gradient-and-ADMM-Ridge-Regression

Use Ridge Regression and Lasso Regression in prostate cancer data

Language: Python - Size: 16.6 KB - Last synced at: about 1 year ago - Pushed at: over 5 years ago - Stars: 5 - Forks: 3

gabrielcs/movie-ratings-prediction

Roger Ebert's movie ratings prediction

Language: Jupyter Notebook - Size: 69.8 MB - Last synced at: almost 2 years ago - Pushed at: almost 8 years ago - Stars: 5 - Forks: 7

AmishaSomaiya/Machine-Learning

Machine Learning Code Implementations in Python

Language: Python - Size: 36.9 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 4 - Forks: 0

varun-soni-ai/LaptopPricePredictionProject

Laptop Price Prediction with Regression Analysis and Exploratory Data Analysis (EDA)

Language: Jupyter Notebook - Size: 1.95 MB - Last synced at: 22 days ago - Pushed at: almost 2 years ago - Stars: 4 - Forks: 0

esharma3/project_austin_air_quality_analysis_and_prediction

The purpose of this project is to analyze the impact of climate change on air quality for the city of Austin and create a machine learning model that can establish a correlation between the level of air pollutants like Ozone and NO2 and the climate parameters by using regression models and null hypothesis.

Language: Jupyter Notebook - Size: 1.57 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 4 - Forks: 0

MHassaanButt/Frequentist-inference-and-regression-modeling

In this project, I am applying your frequentist inference and regression modelling skills to different datasets. I applied several machine learning algorithms and try to answer research questions of related problems and also perform data visualization to justify my results.

Language: Jupyter Notebook - Size: 933 KB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 4 - Forks: 0

ChaitanyaC22/House-Price-Prediction-Project-for-a-US-based-housing-company

The goal of this project is to garner data insights using data analytics to purchase houses at a price below their actual value and flip them on at a higher price. This project aims at building an effective regression model using regularization (i.e. advanced linear regression: Ridge and Lasso regression) in order to predict the actual values of prospective housing properties and decide whether to invest in them or not.

Language: Jupyter Notebook - Size: 3.97 MB - Last synced at: about 2 months ago - Pushed at: almost 4 years ago - Stars: 4 - Forks: 1

TanerArslan/Benchmarking_Classifiers_after_SVM-RFE

Evaluating multiple classifiers after SVM-RFE (Support Vector Machine-Recursive Feature Elimination)

Language: Python - Size: 22.5 KB - Last synced at: about 2 years ago - Pushed at: about 4 years ago - Stars: 4 - Forks: 1

esharma3/fish_weight_prediction_machine_learning

A Regression Model that predicts a fish's weight based on its specie, length, width & height.

Language: Jupyter Notebook - Size: 2.13 MB - Last synced at: about 2 years ago - Pushed at: about 5 years ago - Stars: 4 - Forks: 0

gargargargar/the-happiness-project

2020 Hacklytics Project

Language: Jupyter Notebook - Size: 13.1 MB - Last synced at: 10 months ago - Pushed at: about 5 years ago - Stars: 4 - Forks: 4

antonior92/NarmaxLasso.jl

Algorithms for Lasso estimation of NARMAX models.

Language: Julia - Size: 145 KB - Last synced at: 27 days ago - Pushed at: over 5 years ago - Stars: 4 - Forks: 3

ankitbit/Statistical_Learning_Theory

This repository corresponds to the course "Statistical Learning Theory" taught at the School of Mathematics and Statistics (FME), UPC under the MESIO-UPC-UB Joint Interuniversity Master's Program under the instructor Pedro Delicado

Language: Jupyter Notebook - Size: 40.8 MB - Last synced at: about 2 years ago - Pushed at: almost 6 years ago - Stars: 4 - Forks: 3

sdeepaknarayanan/Machine-Learning 📦

Assignments of the ML Course at IIT Gandhinagar

Language: Jupyter Notebook - Size: 32.6 MB - Last synced at: almost 2 years ago - Pushed at: about 6 years ago - Stars: 4 - Forks: 1

gosuddin/machine-learning-for-finance

ML Coursework focused on solving Computational Finance and Risk Assessment models

Language: Jupyter Notebook - Size: 1.59 MB - Last synced at: over 1 year ago - Pushed at: over 7 years ago - Stars: 4 - Forks: 9

ostad-ai/Machine-Learning

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

Language: Jupyter Notebook - Size: 2.16 MB - Last synced at: 4 days ago - Pushed at: 4 days ago - Stars: 3 - Forks: 0

Nizarassad/Pan-Cancer-Analysis

The Cancer Genome Atlas Pan-Cancer analysis project

Language: Python - Size: 9.48 MB - Last synced at: 30 days ago - Pushed at: 9 months ago - Stars: 3 - Forks: 1

akash1188/Predictive-Analysis-ML

Explore ML mini-projects with Jupyter notebooks. Discover predictive analysis for commercial sales, leveraging regression models such as linear regression, decision trees, random forests, lasso, ridge, and extra-trees regressor.

Language: Jupyter Notebook - Size: 458 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 3 - Forks: 0

yasarigno/Predictions_on_Energy_Consumption_in_Seattle

Prediction on energy consumptions of the city of Seattle in order to reach its goal of being a carbon neutral city in 2050.

Language: Jupyter Notebook - Size: 7.06 MB - Last synced at: about 1 year ago - Pushed at: over 1 year ago - Stars: 3 - Forks: 0

madhurima-nath/regression_and_predictions 📦

Language: HTML - Size: 34.9 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 3 - Forks: 3

shaadclt/Boston-House-Price-Prediction-LassoRegression

This project involves the prediction of house prices in Boston using Lasso Regression in Jupyter Notebook. The dataset contains features such as average number of rooms per dwelling, crime rate, and more. Through this analysis, we aim to build a regression model that accurately predicts house prices based on the given input features.

Language: Jupyter Notebook - Size: 9.77 KB - Last synced at: about 1 month ago - Pushed at: almost 2 years ago - Stars: 3 - Forks: 0

iyashk/Lasso-and-Ridge-using-ADMM

Analyzing LASSO and Ridge regressions using ADMM and Gradient Descent methods. Also the ADMM model is compared with that of Sci-Kit Learn's existing model.

Language: Jupyter Notebook - Size: 1.12 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 3 - Forks: 4

Madhav-Somanath/WindEnergyPredictor

Machine learning model built for IBM Hack 2020 challenge. ⚙️

Language: Jupyter Notebook - Size: 2.03 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 3 - Forks: 1

TNO-MPC/mpyc.secure_learning

TNO PET Lab - secure Multi-Party Computation (MPC) - MPyC - Secure Learning

Language: Python - Size: 66.4 KB - Last synced at: 22 days ago - Pushed at: almost 3 years ago - Stars: 3 - Forks: 0

NisAr-PakhtoOn/4V_Ridge_Lasso_Regression

lasso regression is used in this notebook

Size: 15.6 KB - Last synced at: over 1 year ago - Pushed at: about 3 years ago - Stars: 3 - Forks: 1

helenamin/Perth-City-Properties

This project is about exploring and visualizing data of real estate properties in the City of Perth. It also includes property price range estimation using machine learning.

Language: Jupyter Notebook - Size: 10 MB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 3 - Forks: 1

Labo-Lacourse/Code_chap_23_logistic_regression_regularization

Algorithmes d’apprentissage et modèles statistiques: Un exemple de régression logistique régularisée et de validation croisée pour prédire le décrochage scolaire

Language: Jupyter Notebook - Size: 7.23 MB - Last synced at: over 1 year ago - Pushed at: almost 4 years ago - Stars: 3 - Forks: 6

baked-bytes/Rossmann-Stores

Predicting the daily sales of Rossmann Stores

Language: Jupyter Notebook - Size: 14.6 MB - Last synced at: about 1 month ago - Pushed at: about 4 years ago - Stars: 3 - Forks: 1

thatfreakcoder/IPL-Score-Prediction-with-Machine-Learning

A Jupyter Notebook / Google Colab based Machine Learning Notebook for training a model to predict the first inning score of an IPL match using data from matches played between 2008 to 2017.

Language: Jupyter Notebook - Size: 20.7 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 3 - Forks: 4

sylvaincom/high-dimensional-statistics

[Python, R] My homeworks for the Statistics for high-dimensional data course of my MSc @ Mines Nancy

Language: Jupyter Notebook - Size: 1.2 MB - Last synced at: about 2 months ago - Pushed at: over 5 years ago - Stars: 3 - Forks: 0

xianchen2/lasso_ProximalGD_Accelerated_ADMM

Python Implementations of proximal GD, Accelerated proximal GD and ADMM for solving lasso regression

Language: Jupyter Notebook - Size: 62.5 KB - Last synced at: about 2 years ago - Pushed at: about 6 years ago - Stars: 3 - Forks: 4

DanteSc03/BroachAlign-Machine-Learning

focus on machine learning techniques for clustering and regression analysis. It explores real-world datasets to solve challenges and extract meaningful insights. Specifically, it addresses the critical task of predicting when to replace broaches used in manufacturing airplane engines.

Language: R - Size: 20.7 MB - Last synced at: 11 days ago - Pushed at: 11 days ago - Stars: 2 - Forks: 0

vadimtyuryaev/RegrCoeffsExplorer

A tool for visualizing the coefficients of various regression models, taking into account empirical data distributions.

Language: R - Size: 5.65 MB - Last synced at: 27 days ago - Pushed at: 4 months ago - Stars: 2 - Forks: 0

juan-gamero-salinas/climateready-survey-pamplona

This repository gives you access to the CLIMATEREADY survey dataset containing thermal comfort votes during the 2021 and 2022 heatwave periods in Pamplona, Spain, as well as other relevant parameters self-reported by surveyees (e.g. occupant characteristics and behaviour, key building/dwelling characteristics, sleep problems, heat-related symptoms)

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Dom-Owens-UoB/moseg

Methods for data segmentation under a sparse regression model

Language: R - Size: 874 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 2 - Forks: 1

LuluW8071/Laptop-Price-Prediction

A collection of machine learning models for predicting laptop prices

Language: Jupyter Notebook - Size: 1.5 MB - Last synced at: 2 months ago - Pushed at: over 1 year ago - Stars: 2 - Forks: 0

modal-inria/HDPenReg

Algorithms for lasso and fused-lasso problems: implementation of the lars algorithm for lasso and fusion penalization and EM-based algorithms for (logistic) lasso and fused-lasso penalization.

Language: C++ - Size: 216 KB - Last synced at: 12 days ago - Pushed at: over 1 year ago - Stars: 2 - Forks: 1

Mathurshab2210/Ml_with_Mathur

Here are some fun projects to learn ML using Handson approach

Language: Jupyter Notebook - Size: 2.71 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 2 - Forks: 0

samchak18/Capstone_Project_2_Retail_Sales_Prediction

AlmaBetter Capstone Project -Machine Learning Project type: Regression. Sales forecasting is an approach retailers use to anticipate future sales by analyzing past sales, identifying trends, and projecting data into the future.

Language: Jupyter Notebook - Size: 3.03 MB - Last synced at: over 1 year ago - Pushed at: almost 2 years ago - Stars: 2 - Forks: 1

connect-midhunr/rossmann-sales-prediction

Machine learning model to forecast the sales of each Rossmann store for any given date.

Language: Jupyter Notebook - Size: 103 MB - Last synced at: about 1 year ago - Pushed at: about 2 years ago - Stars: 2 - Forks: 1

benjamin-milhet/F1_Machine_Learning

Dans ce projet, nous allons explorer différents algorithmes de Machine Learning pour prédire le nombre de points qu’un pilote de Formule 1 peut gagner lors d’un Grand-Prix. Nous allons utiliser des données de Formule 1 depuis 1950 pour entraîner nos modèles de prédiction.

Language: Jupyter Notebook - Size: 3.21 MB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 2 - Forks: 0

ZJW-92/Ames_House_Price_Prediction

This is a house price prediction study which utilized Exploratory Data Analysis, Dealing with Missing Values, Linear Regression with LASSO and Ridge regularization to predict house prices in the Ames Housing Data Set

Language: Jupyter Notebook - Size: 1.3 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 0

gauravkapoor27/LASSO-Feature-Selection

Language: Jupyter Notebook - Size: 1.47 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 0

Masihsoniya/House-price-prediction

Predicting house price

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cankobanz/understanding-supercapacitor-charge-discharge-rates-using-machine-learning

The project aims to design machine learning algorithm which is able to predict energy densities of supercapacitors by using input data that is enriched from the results came from image processing techniques.

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ROCCYK/Regression

Intro to Machine Learning Assignment 1

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MouadEttali/R_Shiny_Study

Employee Attrition Predictor in R Shiny based on the HR-employee-Attration Data in kaggle

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abduliante/vehicle-default-loan-prediction

Forecasting the likelihood of a customer defaulting their auto loan using classification models

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ahmedshahriar/Housing-Price-Prediction

Data science project on Housing Prices Dataset regression analysis

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keivanipchihagh/ML-Models-from-Scratch

Implementations of Machine Learning models, Regularizers, Optimizers and different Cost functions.

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anikch/house-price-prediction-Ridge_Lasso

Housing price prediction model using Ridge and Lasso Regression.

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tboudart/Life-Expectancy-Regression-Analysis-and-Classification

I contributed to a group project using the Life Expectancy (WHO) dataset from Kaggle where I performed regression analysis to predict life expectancy and classification to classify countries as developed or developing. The project was completed in Python using the pandas, Matplotlib, NumPy, seaborn, scikit-learn, and statsmodels libraries. The regression models were fitted on the entire dataset, along with subsets for developed and developing countries. I tested ordinary least squares, lasso, ridge, and random forest regression models. Random forest regression performed the best on all three datasets and did not overfit the training set. The testing set R2 was .96 for the entire dataset and developing country subset. The developed country subset achieved an R2 of .8. I tested seven different classification algorithms to classify a country as developing or developed. The models obtained testing set balanced accuracies ranging from 86% - 99%. From best to worst, the models included gradient boosting, random forest, Adaptive Boosting (AdaBoost), decision tree, k-nearest neighbors, support-vector machines, and naive Bayes. I tuned all the models' hyperparameters. None of the models overfitted the training set.

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tboudart/Financial-Markets-Regression-Analysis

My role in this group project was to perform regression analysis on quarterly financial data to predict a company's market capitalization. I used R to develop ordinary least squares (OLS), stepwise, ridge, lasso, relaxed lasso, and elastic net regression models. I first used stepwise and OLS regression to develop a model and examine its residual plots. The plot displaying the residuals against the predicted values indicated multiplicative errors. I, therefore, took the natural log transformation of the dependent variable. The resulting model's R2 was significantly, negatively impacted. After examining scatter plots between the log transformation of market capitalization and the independent variables, I discovered the independent variables also had to be transformed to produce a linear relationship. Using the log transformation of both the dependent and independent variables, I developed models using all the regression techniques mentioned to strike a balance between R2 and producing a parsimonious model. All the models produced similar results, with an R2 of around .80. Since OLS is easiest to explain, had similar residual plots, and the highest R2 of all the models, it was the best model developed.

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