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

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

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

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

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

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

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

INN Hotels Project

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

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

Traditional Regression problem project in Python

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.

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

Fundamentals of Machine Learning Assignment Repository

Python with Tableau

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.

📈 Hands-on regression analysis project in R using a dataset with 30 predictors. Includes manual OLS implementation without lm(), p-value computation, and comparison with built-in functions. Applies stepwise selection (AIC/BIC), Ridge, and Lasso to minimize test error and identify key predictors.

This is an linear approach machine learning model used to predict the values of variable(dependent) based on other variables(independent).

Advanced ML & DL project analyzing global health and socio-economic indicators to predict life expectancy.