SENATOROVAI/Normal-equation-solver-multiple-linear-regression-course
Multiple Linear Regression (MLR) models the linear relationship between a continuous dependent variable and two or more independent (explanatory) variables. Using the equation, it predicts outcomes based on multiple factors. Key assumptions include linearity, constant variance of residuals, and low correlation between independent variables.Solver
Stars
16
Forks
14
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 01, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/SENATOROVAI/Normal-equation-solver-multiple-linear-regression-course"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
SENATOROVAI/Normal-equations-scalar-form-solver-simple-linear-regression-course
The normal equations for simple linear regression are a system of two linear equations used to...
SENATOROVAI/underfitting-overfitting-polynomial-regression-course
Underfitting and overfitting are critical concepts in machine learning, particularly when using...
andrescorrada/IntroductionToAlgebraicEvaluation
A collection of essays and code on algebraic methods to evaluate noisy judges on unlabeled test data.
stabgan/Multiple-Linear-Regression
Implementation of Multiple Linear Regression both in Python and R
TatevKaren/data-science-popular-algorithms
Data Science algorithms and topics that you must know. (Newly Designed) Recommender Systems,...