Small example on how you can detect multicollinearity

R package to manage multicollinearity in modeling data frames.

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

Quadratic programming feature selection

This repository shows how Lasso Regression selects correlated predictors

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

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

R function to detect multicollinearity in ERGM

Linear regression on numerical attributes

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.

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.

Classification problem using multiple ML Algorithms

Android malware detection using machine learning.

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

A Regression Exercise covering OLS & Ridge Regression

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

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

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

INN Hotels Project

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