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multiple-regression

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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.

  • Updated Aug 17, 2023
  • Python

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

  • Updated Dec 8, 2023
  • Jupyter Notebook

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…

  • Updated Jun 9, 2020
  • Jupyter Notebook

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.

  • Updated Dec 18, 2023
  • Jupyter Notebook
Real-Estate-Statistical-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.

  • Updated Sep 29, 2021
  • Jupyter Notebook

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 …

  • Updated Jul 25, 2022

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)

  • Updated Feb 6, 2020
  • Jupyter Notebook

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