Learning to create Machine Learning Algorithms

This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This library can be used for key driver analysis or marginal resource allocation models.

Built house price prediction model using linear regression and k nearest neighbors and used machine learning techniques like ridge, lasso, and gradient descent for optimization in Python

Recursive Leasting Squares (RLS) with Neural Network for fast learning

Testing doing basic regression with web assembly

In this project you will build and evaluate multiple linear regression models using Python. You will use scikit-learn to calculate the regression, while using pandas for data management and seaborn for data visualization. The data for this project consists of the very popular Advertising dataset to predict sales revenue based on advertising spen…

Machine-Learning-Regression

PYTHON- Projects in my MAT-243 STATS for STEM I course at SNHU (HTML files and Python files with source code and reports)

A collection of some of my R Projects

Data analysis with Python to building and evaluating data models.

Quantitative Finance & Statistics Projects. Topics including multiple linear regression, variance and instability estimates, display methodology.

A simple intuitive method for multiple regression

In this repository, delve into the realm of regression modeling featuring an array of algorithms applied to diverse datasets. Explore the strengths and nuances of different regression techniques, providing a comprehensive overview for anyone interested in predictive modeling.

Predictive analysis, with feature engineering, and machine learning (ML) algorithms, such as linear regression, applied to predict the final sale price of homes in Ames, IA from 2006-2010.

Examples of Machine Learning Regression Models Built in Python and R

I constructed a simulation study to evaluate the statistical performance of two equivalence-based tests and compared it to the common, but inappropriate, method of concluding no effect by failing to reject the null hypothesis of the traditional test. I further propose two R functions to supply researchers with open-access and easy-to-use tools …

DataScienceOverHood

Multiple regression is an extension of simple linear regression. It is used when we want to predict the value of a variable based on the value of two or more other variables. The variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable)

Basics of Machine Learning

Linear Regression and polynomial regression using Python