R package to manage multicollinearity in modeling data frames.

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

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

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.

Linear regression on numerical attributes

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

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

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.

Android malware detection using machine learning.

A Regression Exercise covering OLS & Ridge Regression

This project analyzes and preprocesses the Online Retail dataset to uncover insights into customer purchasing behaviors, sales trends, and product performance. It includes data cleaning, exploration, and visualization, with the goal of enhancing understanding of online retail dynamics.

INN Hotels Project