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Linear Regression Machine Learning Visualizer

STEM Interactive Visual Learning Program at TEC-Bridge AI

Setup Data

Regression Controls

Visualization

Blue dots: Data points, Red line: Current regression line

Parameters

Algorithm Steps

How to Use

  1. Setup: Enter x,y data pairs or click "Sample Data"
  2. Learning Rate: Set the learning rate (0.001-0.1)
  3. Start: Click "Start" to begin gradient descent
  4. Step Through: Click "Next" to see each iteration or "Run Through" for automatic execution
  5. Observe: Watch the line fit the data and cost decrease
  6. Reset: Click "Reset" to start over

Linear Regression

Linear Regression finds the best-fitting straight line through data points using gradient descent optimization.

How it works:

  • Model: y = mx + b (slope m, intercept b)
  • Cost function: Mean Squared Error (MSE)
  • Gradient descent updates parameters iteratively
  • ∂m = (2/n) × Σ(predicted - actual) × x
  • ∂b = (2/n) × Σ(predicted - actual)
  • Update: m = m - α×∂m, b = b - α×∂b

Goal: Minimize prediction error

Linear Regression Code Implementation

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