Small example on how you can detect multicollinearity

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.

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

R package to manage multicollinearity in modeling data frames.

Android malware detection using machine learning.

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

A Regression Exercise covering OLS & Ridge Regression

INN Hotels Project

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

Traditional Regression problem project in Python

Classification problem using multiple ML Algorithms

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…

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