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

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.

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

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

Modeling King County Home Prices via Multiple Linear Regression

Prediction-with-Multiple-linear-Regression

Multiple Logistic Regression on churn dataset.

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

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.

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.

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.

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.