yizenglistat/glmcs

Generalized Linear Models with Confidence Sets

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francescopiocirillo/linear-regression-from-scratch-R

Hands-on regression analysis project in R using a dataset with 30 predictors. Includes manual OLS implementation without lm(), p-value computation, and comparison with built-in functions. Applies stepwise selection (AIC/BIC), Ridge, and Lasso to minimize test error and identify key predictors.

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0xnu/multicollinearity_llm 📦

A multicollinearity-based compression C program, identifies and removes highly correlated weights in neural networks, thereby reducing redundancy.

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subu53/Ames-Housing-Price-Prediction-Ml-Regression

House price prediction using regression machine learning models

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jibbs1703/King-County-House-Prices

This repository contains a price prediction model for King County, Washington. The model results and analysis would prove invaluable to investors and stakeholders in the King County, WA housing market.

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Morano-git/WDBC-ML-Classification-Assignment

Fundamentals of Machine Learning Assignment Repository

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FedeGambe/Master_s_thesis_Data_science

Questa repository contiene il codice e i materiali relativi alla tesi magistrale, con un focus su analisi statistiche ed analisi predittive. Include strumenti e metodi per esplorare e modellare i dati, con tecniche statistiche avanzate come la regressione logistica, analisi di clustering, e metodi di ML e DL per la previsione e classificazione

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BlasBenito/collinear

R package to manage multicollinearity in modeling data frames.

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favstats/multicol_sim

Analyzing Multicollineaerity with a little simulation

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Edanur-Y/Variable-Analysis-of-Banks-Ratio-Data

Testing variables for multicollinearity, multivariate normality and analyzing outliers and missing values. ⭕SPSS 🔵R

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HMesghali/Biogas-Production-Machine-Learning-Analysis

Machine learning approach for feature selection and uncertainty analysis in wastewater treatment plant biogas production. Explores advanced ML techniques for optimizing renewable energy processes.

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VivekSagarSingh/Probability-of-Credit-card-Default

Classification problem using multiple ML Algorithms

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zohrabgulushev/Data-Science-Project

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prneidhardt/Supervised-Learning-Classification

INN Hotels Project

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Akashash01/Akash_Linear-regression

This is an linear approach machine learning model used to predict the values of variable(dependent) based on other variables(independent).

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manjugovindarajan/INNHOTELS-supervisedlearningclassifications-

Analyze INN Hotels data to find which factors have a high influence on booking cancellations, build a predictive model to predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.

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BNTechie/Regression_analysis

house price prediction, Comparison of Ml algorithm, Logistic regression, Multicollinearity, Multivariate regression analysis, Linear model with random effects, Robust regression

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c0ra/RRSARMMI

This repository contains the code and data necessary to reproduce the results presented in the paper "Ridge Regularization for Spatial Auto-regressive Models with Multicollinearity Issues" submitted to Advances in Statistical Analysis (AStA).

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aayushi-droid/Multicollinearity-in-Regression-Analysis

Multicollinearity in Regression Analysis

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Avinash793/regression-analysis-examples

Detailed implementation of various regression analysis models and concepts on real dataset.

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gepapago/Empirical-Research

Basic methodologies of Empirical Research applied on various case studies (R language)

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oliviergimenez/p2cr

Fit principal component capture-recapture model to snow petrel data

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UzoigweC/INNHotels

Analyze the data of INN Hotels to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds

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Vaneeza-7/Bayesian-Statistics-Regression-Models-in-pymc3 📦

Bayesian Statistics: Linear Regression and multi-linear models and related concepts (multicollinearity, correlation coefficient etc) on iris dataset in pymc3

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eeshwarib23/Airbnb-regression-analysis

ML | Regression Analysis| Random Forest| XGBoost| Gradient Boost| EDA| Feature Engineering| Feature selection

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jmersinger/EPI-vs-GDP-Data-Analysis-Visualization-Paper

Research, Analysis, and Final Paper for my Intro to Econometrics class taken in Fall 2023

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OmBaval/Airline-Customer-Satisfaction

This project employs a dataset of 103,904 entries with 25 features. Utilizing the XGBoost classifier,The workflow involves data fetching, feature selection, preprocessing, correlation analysis, best feature selection, data rescaling, train-test split, and target balancing. Predicts whether a customer will experience satisfaction with a flight.

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venkatesh-eranti/Housing_case-study

A real estate company that has a dataset containing the prices of properties in the Delhi region. It wishes to use the data to optimise the sale prices of the properties based on important factors such as area, bedrooms, parking, etc

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rojaff/dredge_mc

Assess multicollinearity between predictors when running the dredge function (MuMIn - R)

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ashishyadav24092000/Multicollinearity-in-Regression

Showing how to identify multicollinearity in a regression problem using the OLS(Ordiniary Least Square Method) and correlation chart adn finaly eradicating it.

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bhattbhavesh91/multicollinearity_detection

Small example on how you can detect multicollinearity

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NicholasDominic/Stochastic-VIF-ID-Rice-SNPs

By leveraging ensemble learning, this program can be used to analyze the Linkage Disequilibrium between SNPs in each Indonesian rice chromosomes. Developed using Python 3.9.12.

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esmailza/Housing_Price

A simple Neural Network Model to predict the housing price based on the house features like bedrooms, area, etc. We are using kaggle Housing Prices Dataset. The data has multicollinearity prob

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SarangGami/TED-Talks-Views-Prediction-Supervised-learning

This project aims to build a regression model that predicts the number of views for TED Talks videos on the TED website.

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SarangGami/Bank-Marketing-Effectiveness-Prediction-supervised-learning

The main objective is to build a predictive model that predicts whether a new client will subscribe to a term deposit or not, based on data from previous marketing campaigns.

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olesyamba/ICvsML

Usual linear regression or XGBoost? Combo! Or how I was investigating the impact of intellectual capital on NASDAQ-100 capitalization during 2 years.

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alef-s/INN_hotels

Analyze the data of INN Hotels to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.

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mjkim1001/AS21

Applied Statistics I, 2021, UNC at Chapel Hill-linear regression

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Niranjan-stat/Regression-Analysis-on-Drinking-Data

Linear regression, VIF, Auto Correlation.

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WuCandice/-Statistical-Analysis-of-Economic-Variables-and-the-Mortgage-Rate-in-the-United-States

This project is about to use linear regression to examine the relationship between various economic variables and the mortgage rate in the United States.

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mamomen1996/Python_CS_01

Traditional Regression problem project in Python

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babusarath05/multi_corr

multi_corr helps to identify multicollinearity in a simple and straight manner.

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Yemi-Ak/Supervised-Learning-Booking-Cancellations-INNHotels

The aim is to analyze the data of INN Hotels to find which factors have a high influence on booking cancellations, build predictive models(logisitic regression, decision trees) that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.

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sachelsout/effect-of-collinear-features-on-linear-models

This repository shows, how linear models behave if the features of the dataset are collinear in nature. Support Vector Machine(SVM) and Logistic Regression(LR) algorithms are used as linear models. Weights and accuracy scores are recorded in different scenarios.

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bhattbhavesh91/pca-multicollinearity

A simple example to show how Principal Component Analysis can be used to Address Multicollinearity

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Lori10/BostonHousing-Using-LinearRegreesion-Ridge-Lasso-ElasticNet

In this repo I have implemented a machine learning project which predicts the house price in Boston. I have covered these topics : Exploratory Data Analysis, Feature Engineering including feature scaling, transformation into normally distributed data, multicollinearity, feature selection. I have trained the dataset using Linear Regression, Ridge, Lasso, and Elastic Net Regression.

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sduxbury/vif-ergm

R function to detect multicollinearity in ERGM

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uweremer/regression_diagnostics

Skript zur Videoreihe Regressionsdiagnostik in R

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sachin17git/Malware-detection-ML

Android malware detection using machine learning.

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being-aerys/Data_Processing_and_Feature_Engineering_in_Machine_Learning

This is an attempt to summarize feature engineering methods that I have learned over the course of my graduate school.

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bhattbhavesh91/lasso-regression-python

This repository shows how Lasso Regression selects correlated predictors

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petermchale/predict_customer_response

Machine-learning models to predict whether customers respond to a marketing campaign

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dayanacavalcante/Multicollinearity_VIF

Quantify multicollinearity by VIF

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chinmayeeguru/Bike-Sharing-Linear-Regression-Model

To model the demand for shared bikes with the available independent variables

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devosmitachatterjee2018/Linear_Statistical_Models

The project involves the multivariate regression analysis of a dataset.

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ahmed-arafaath/Ad_clicks_predictor

An ML model to predict Ad viewers based on various factors.

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swarnava-96/Linear-Ridge-and-Lasso-Regression

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daniel-furman/RecFeatureSelect

Feature selection functions (1) using the multi-collinearity matrix and recursively proceeding to a spearman threshold and (2) using Forward Stepwise Selection running on an ensemble sklearner (with options for HPO).

Language: Python - Size: 1.84 MB - Last synced at: about 2 months ago - Pushed at: about 4 years ago - Stars: 0 - Forks: 0

bhattbhavesh91/regression-excercise-ols-ridge

A Regression Exercise covering OLS & Ridge Regression

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AnFrBo/suicidal_actions

Analysis of Influencing Factors Leading to Suicidal Actions via Linear Regression and Regularization Methods

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govardhan26/Linear-regression

Linear regression on numerical attributes

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amkatrutsa/QPFeatureSelection

Quadratic programming feature selection

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ankitbit/Linear_and_Generalized_Linear_Models

This repository has scripts that are part of the programming assignments of the course Linear and Generalized Linear Models taught at FME, UPC Barcelonatech.

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ireneliu521/BOPS-Strategy-Analysis_Project_R

Evaluate the Buy Online Pick-up in Store (BOPS) strategy with a real-world dataset

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