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

Modeling King County Home Prices via Multiple Linear Regression

A Python code for data analysis and salary predictions using a multiple linear regression model. The code calculates the intercept and coefficients of the model and makes predictions on sample data.

Prediction-with-Multiple-linear-Regression

Stepwise Multiple Linear Regression (With Interactions) and Random Forest Regression on predicting the Productivity of the Garment Factory Workers

Improve targeted advertising of a popular streaming service in exploring new approaches using Neural Network

A MLR algorithm that analyzes diabetes data in African Americans to predict a diabetes diagnosis

Multiple Logistic Regression on churn dataset.

A Python implementation of multiple linear regression to predict the profit of startups based on their spending in R&D, Administration, Marketing, and the state they operate in.

https://ccrodriguez27.github.io/Population-Growth-Prediction-using-Multiple-Linear-Regression/

Software to calculate the direct and indirect effects of climate change on health and economy by using a multiple linear model and for a South Asia country case study

Your all-in-one Machine Learning resource – from scratch implementations to ensemble learning and real-world model tuning. This repository is a complete collection of 25+ essential ML algorithms written in clean, beginner-friendly Jupyter Notebooks. Each algorithm is explained with intuitive theory, visualizations, and hands-on implementation.

Exploratory Data Analysis of US Wildfires and Drought with R using Linear Regression

This project estimates a multiple linear regression of 50 startups and how their expenses on R & D, administration, marketing, and location can be significant or not to their profits.

Using Multiple Linear Regression, the data set is analysed to determine which independent variables provide a non-random amount of variance to the dependent variable and conclude whether a linear model would be sufficiently predictive.

This repository contains a comprehensive implementation of gradient descent for linear regression, including visualizations and comparisons with ordinary least squares (OLS) regression. It also includes an additional implementation for multiple linear regression using gradient descent.

Investigate the response variable (dependent variable) life expectancy in the year 2016 and use other indicators (predictor variables) of the dataset to develop a linear model which explains the life expectancies 2016.