mahdi-eth/Linear-Regression-from-Scratch
This project implements a Python-based linear regression model from scratch, complete with custom functions for mean squared error and gradient descent algorithm. It is tested on data, using features to predict target variables. The project offers a practical introduction to linear regression.
This project helps data scientists and machine learning engineers understand the core mechanics of linear regression by providing a transparent, from-scratch implementation. You provide a dataset with numerical features and a target variable, and it outputs a model that can predict future values, along with an explanation of how the predictions are made. It's ideal for those learning or teaching the foundational algorithms of predictive modeling.
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Use this if you are a student, educator, or practitioner looking to deeply understand how a linear regression model works under the hood without relying on black-box libraries.
Not ideal if you need a production-ready, highly optimized, or feature-rich linear regression implementation for real-world applications.
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Jupyter Notebook
License
MIT
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Last pushed
Mar 28, 2025
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