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.

R package to manage multicollinearity in modeling data frames.

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.

Classification problem using multiple ML Algorithms

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.

Python with Tableau

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

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.