Skip to content

[FEATURE] Add Multiple Linear Regression Implementation with Gradient Descent #2691

@Osamsami

Description

@Osamsami

Is your request related to a problem?
After proposing Simple Linear Regression, I realized that the repository also lacks Multiple Linear Regression (MLR). In real-world data science, we rarely work with a single feature, so having a multi-variable regression example is essential for learners.

Describe the solution you'd like
I want to contribute a Python implementation of Multiple Linear Regression. The script will handle:

  • Vectorized Operations: Using matrices to handle multiple features efficiently.
  • Cost Function: Calculating the error for multiple variables.
  • Gradient Descent: Updating multiple weights ($w_1, w_2, \dots, w_n$) and the bias ($b$) simultaneously.

Describe alternatives you've considered
One could use sklearn.linear_model.LinearRegression, but implementing it from scratch using NumPy/Math logic helps students understand the "Matrix Multiplication" behind Machine Learning.

Example Use Case
Predicting a target value (e.g., House Price) based on multiple inputs like Area, Number of Bedrooms, and Age of the property.

Additional Context
Since I am already working on the Simple Linear Regression PR, I can extend this logic to Multiple Linear Regression to provide a complete "Regression Suite" for this repository.

Metadata

Metadata

Assignees

No one assigned

    Labels

    enhancementNew feature or request

    Projects

    No projects

    Milestone

    No milestone

    Relationships

    None yet

    Development

    No branches or pull requests

    Issue actions