This project demonstrates the concepts of linear regression, specifically focusing on least squares regression and orthogonal linear regression, using both symbolic mathematics and Python libraries.

The fundamental technique has been studied for decades, thus creating a huge amount of information and alternate variations that make it hard to tell what is key vs. non-essential information.

This project applies simple and multiple linear regression techniques using Python and scikit-learn to predict house prices based on numerical features. It is part of my AI & ML internship focused on ...

Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 4.36e+05. This might indicate that there are strong ...

The goal of a time series regression problem is best explained by a concrete example. Suppose you own an airline company and you want to predict the number of passengers you'll have next month based ...

Often the questions we ask require us to make accurate predictions on how one factor affects an outcome. If a teacher is asked to work out how time spent writing an essay affects essay grades, it’s ...