multicollinearity

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.

BlasBenito/collinear

R package to manage multicollinearity in modeling data frames.

amkatrutsa/QPFeatureSelection

Quadratic programming feature selection

bhattbhavesh91/lasso-regression-python

This repository shows how Lasso Regression selects correlated predictors

bhattbhavesh91/pca-multicollinearity

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

petermchale/predict_customer_response

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

sduxbury/vif-ergm

R function to detect multicollinearity in ERGM

sachin17git/Malware-detection-ML

Android malware detection using machine learning.

Avinash793/regression-analysis-examples

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

govardhan26/Linear-regression

Linear regression on numerical attributes

raghav19980730/DataCo-Supply-Chain-Goods-Delivery-Prediction

The main objective of this project is to build a model to identify whether the delivery of an order will be late or on time.

VivekSagarSingh/Probability-of-Credit-card-Default

Classification problem using multiple ML Algorithms

0xnu/multicollinearity_llm

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

bhattbhavesh91/regression-excercise-ols-ridge

A Regression Exercise covering OLS & Ridge Regression

prneidhardt/Supervised-Learning-Classification

INN Hotels Project

rojaff/dredge_mc

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

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.

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.

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).

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.

Allan34Kirwa/Predicting-Apartment-Prices-in-Mexico-City-MX

This repo implements a machine learning model to predict real estate prices in Mexico City. It preprocesses data, incorporates one-hot encoding, imputation, and Ridge regression, achieving accurate price approximations.

BNTechie/Regression_analysis

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

chinmayeeguru/Bike-Sharing-Linear-Regression-Model

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

deeprpatel700/Regression_Analysis_-_Factors_affecting_Life_Expectancy

Statistical Multivariate Regression Analysis to determine the effects of mortality, economic and social factors on life expectancy.

eeshwarib23/Airbnb-regression-analysis

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

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.

mamomen1996/Python_CS_01

Traditional Regression problem project in Python

RudraChatterjee/PricePrediction_Regression

Explored the dataset of a company that specializes in the reselling of used and refurbished devices. The objective of this project was to determine the future price of used phones and identify the factors that significantly influence them using a linear regression model with python

utkarsh-n/Global-Investment-Modeling

RStudio project utilizing various statistical methods to replicate and diagnose the findings of Appel and Loyle from their study on post-conflict justice and foreign direct investment.

Vinitk93/Effect_InternationalAid_Economy

Python with Tableau

WuCandice/Statistical-Analysis-on-US-Mortgage-Rates-Using-R

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

mayankbrn/9.8_Jamboree_Linear_Regression

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

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

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

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.